Gender differences in predictors of initiation, retention, and completion in an HMO-based substance abuse treatment program

Journal of Substance Abuse Treatment 23 (2002) 285 – 295

Regular article

Gender differences in predictors of initiation, retention, and completion in an HMO-based substance abuse treatment program Carla A. Green, Ph.D., M.P.H.a,b,*, Michael R. Polen, M.A.b, Daniel M. Dickinson, M.A., CADC II, NCAC IIc, Frances L. Lynch, Ph.D., M.S.P.H.b, Marjorie D. Bennett, M.A.b a

Oregon Health & Science University, Department of Public Health & Preventive Medicine, 3181 S.W. Sam Jackson Park Rd., Portland, OR 97201-3098, USA b Kaiser Permanente Center for Health Research, 3800 North Interstate Ave., Portland, OR 97227, USA c Department of Addiction Medicine, Kaiser Permanente Northwest, 3550 North Interstate Ave., Portland, OR 97227, USA Received 2 January 2002; received in revised form 7 June 2002; accepted 24 June 2002

Abstract We studied gender differences in treatment process indicators among 293 HMO members recommended for substance abuse treatment. Treatment initiation, completion, and time spent in treatment did not differ by gender, but factors predicting these outcomes differed markedly. Initiation was predicted in women by alcohol diagnoses; in men, by being employed or married. Failure to initiate treatment was predicted in women by mental health diagnoses; in men, by less education. Treatment completion was predicted in women by higher income and legal/agency referral; in men, by older age. Failure to complete was predicted in women by more dependence diagnoses and higher Addiction Severity Index Employment scores; in men, by worse psychiatric status, receiving Medicaid, and motivation for entering treatment. More time spent in treatment was predicted, in women, by alcohol or opiate diagnoses and legal/agency referral; in men, by fewer mental health diagnoses, higher education, domestic violence victim status, and prior 12-step attendance. Clinical implications of results are discussed. D 2002 Elsevier Science Inc. All rights reserved. Keywords: Gender; Substance Abuse; Treatment initiation; Treatment retention; Treatment completion

1. Introduction A ‘‘successful’’ treatment episode can be characterized by three distinct phases, each of which presents opportunities for either success or failure. First, a client must contact the treatment system and access services (treatment access). Second, after contact has been made and a recommendation for treatment received, the client must begin attending treatment sessions (treatment initiation). Third, the client must stay in treatment long enough to complete the program, including aftercare (treatment completion). Factors that interfere with any of these phases are likely to lead to worse outcomes; for this reason, treatment process is of critical interest to practitioners. In recent years, concerns * Corresponding author. Kaiser Permanente Center for Health Research, 3800 N. Interstate Avenue, Portland, OR 97227, USA. Tel.: +1-503-3352479; fax +1-503-335-2424. E-mail address: [email protected] (C.A. Green).

have been raised regarding the effects of gender and genderrelated circumstances on treatment processes and outcomes (Dawson, 1996; Hodgins, el Guebaly, & Addington, 1997; Jarvis, 1992). Consumers, researchers, and practitioners have all argued that many of the factors critical to successful treatment differ for men and women, and that women are more likely to experience circumstances that interfere with their ability to successfully navigate treatment processes (Brown, Alterman, Rutherford, Cacciola, & Zaballero, 1993; Chatham, Hiller, Rowan-Szal, Joe, & Simpson, 1999; Rowan-Szal, Chatham, Joe, & Simpson, 2000; Schmidt & Weisner, 1995; Smith & Weisner, 2000; Weisner & Schmidt, 1992; Wilsnack & Wilsnack, 1997). Diagnostic systems, standard treatment methods, and substance-abuse treatment research have been criticized as being male-oriented and non-responsive to women (Floyd, Monahan, Finney, & Morley, 1996; Reed, 1987). Correspondingly, consumers, researchers, and practitioners who work with substance-abusing women have called for

0740-5472/02/$ – see front matter D 2002 Elsevier Science Inc. All rights reserved. PII: S 0 7 4 0 - 5 4 7 2 ( 0 2 ) 0 0 2 7 8 - 7

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improved research, case finding, and diagnosis/assessment of women (Amodei, Williams, Seale, & Alvarado, 1996; Copeland & Hall, 1992; Jarvis, 1992; Mendelson & Mello, 1998; Smith & Weisner, 2000; Thom, 1984; Walitzer & Connors, 1997; Wilke, 1994) and for the development of gender-sensitive and gender-specific treatment programs (Beckman & Amaro, 1984; Beckman & Kocel, 1982; Hodgins et al., 1997). The result has been increased federal funding for treating women with substance-related problems and for studies specific to women’s treatment needs and treatment outcomes (Schmidt & Weisner, 1995). Over the last decade, however, research has provided only partial support for the above contentions about gender disparities. Additionally, many treatment programs have worked to increase their sensitivity to and improve services for women, and it is no longer clear if the former criticisms still apply. Thus, we do not fully understand how gender differences currently affect treatment processes and outcomes, nor how to design treatment programs to optimally meet the needs of women (Smith & Weisner, 2000). What is known about gender and treatment process is summarized below, with attention to treatment access, initiation, and retention. 1.1. Gender and treatment-seeking for substance-related problems Women are less likely than men to seek treatment for substance abuse problems (Dawson, 1996; Schober & Annis, 1996; Walitzer & Connors, 1997). This observation contrasts with gender differences found in use of other health care services, where women have been shown to need and use more medical care than men (Green & Pope, 1999; Ladwig, Marten-Mittag, Formanek, & Dammann, 2000; Verbrugge, 1985; Verbrugge & Patrick, 1995). Researchers have hypothesized that women seek substance abuse treatment less often than men because they experience: (a) more frequent barriers to treatment, such as childcare responsibilities, inadequate health insurance, and poverty (Hodgins et al., 1997); (b) cultural norms that result in greater stigma for women’s addictions (Center for Substance Abuse Treatment, 1994; Schober & Annis, 1996; Thom, 1986); and (c) inconsistencies between female gender roles and the health problem for which services are being sought, such as conflicts between the ‘‘mother’’ role and seeking assistance for drug abuse problems (Russo & Sobel, 1981). Discomfort resulting from the latter two factors may explain why women are more likely than men to receive substance abuse treatment in mental health settings (Weisner & Schmidt, 1992) — it is possible that women feel less stigmatized or role-discordant in these settings. Thus, research suggests that service seeking is systematically affected by gender and gender-related characteristics, even though the reasons for differences in treatment seeking are not fully understood (Weisner & Schmidt, 2001).

1.2. Gender, treatment initiation, and client characteristics It is not clear if gender plays an important role in treatment initiation after clients make contact with treatment systems (e.g., call for an intake appointment) or after they are assessed and recommended for substance abuse treatment. To date, research results have been contradictory. Festinger and colleagues found no gender differences in assessment attendance among those who called to make intake appointments (Festinger, Lamb, Kountz, Kirby, & Marlowe, 1995), while Weisner et al. found that, among clients who were dependent on alcohol alone, women were more likely than men to start treatment (Weisner, Mertens, Tam, & Moore, 2001). Gender differences among those who do begin treatment are plentiful, however. Women entering treatment appear to have less social support and more family responsibilities than men entering treatment. Women are more likely than men to be concerned about child-related issues (Wechsberg, Craddock, & Hubbard, 1998) and are less likely than men to be married or have spouses who refer them to treatment (Blum, Roman, & Harwood, 1995). Additionally, men who receive suggestions to cut down or stop drinking are more likely to enter treatment, while such suggestions do not predict treatment entry for women (Weisner, 1993). Women who enter treatment for alcohol-related problems are more likely than men to identify factors other than drinking (e.g., stressful life events, mental health symptoms) as their primary problems and are also more likely to report shame and embarrassment upon treatment entry (Thom, 1986, 1987). Women also are more likely than men to have reported experiencing abuse of various kinds (Pettinati, Rukstalis, Luck, Volpicelli, & O’Brien, 2000; Wechsberg et al., 1998). In terms of functioning and health, women seeking treatment have been found to have more substance-related problems, and those problems tend to be more severe than those of men entering treatment (Arfken, Klein, di Menza, & Schuster, 2001; Farid & Clarke, 1992). It is possible that some of these increased difficulties are related to women’s tendency to drink alcohol and use cocaine more frequently than men who have problems related to the same substances (Pettinati et al., 2000; Wechsberg et al., 1998). Women entering treatment are also likely to be younger, to have lower education levels, and to have lower incomes than men (Brady, Grice, Dustan, & Randall, 1993; Wechsberg et al., 1998; Weisner & Schmidt, 1992). They also report more severe depressive symptoms when depressed, and more physical and mental health problems (Brady et al., 1993; Pettinati, Pierce, Wolf, Rukstalis, & O’Brien, 1997). One explanation of women’s greater reported burden of substance-related difficulties is that only those women who have the most severe problems overcome barriers to seeking treatment or come to the attention of institutions that either mandate or strongly encourage treatment.

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1.3. Treatment retention and completion Receiving more substance abuse treatment generally leads to better outcomes (Hubbard, Craddock, Flynn, Anderson, & Etheridge, 1997; Luchansky, He, Krupski, & Stark, 2000; Pettinati et al., 1996). For this reason, identifying, understanding, and addressing factors that differentially affect treatment retention and completion among men and women are crucial parts of programs designed to improve substance abuse treatment outcomes. Based on work to date, it appears that gender and gender-related differences also have important effects on the length of time spent in treatment. Women are more likely than men to drop out of substance abuse treatment (although this appears to be changing) (Arfken et al., 2001; Mammo & Weinbaum, 1993; Simpson et al., 1997; Stark, 1992) and to attend fewer treatment sessions than men do (McCaul, Svikis, & Moore, 2001). However, the ways in which gender affects time spent in treatment should be understood as a series of complex relationships between gender, the treatment process or modality, and personal and social factors (Stark, 1992). For example, DATOS (Drug Abuse Treatment Outcome Study) researchers found that men were more likely to drop out of outpatient drug-free programs, while women were more likely to be categorized in the low-retention group for outpatient methadone treatment (Simpson et al., 1997). A later DATOS article reported greater treatment retention among women than men in outpatient drug-free programs, but no direct gender relationship in long-term residential or outpatient methadone treatment modalities (Joe, Simpson, & Broome, 1999). Other researchers have explored gender differences in the factors that predict treatment retention. Mertens and Weisner (2000) found that both men and women who had less severe substance-related problems stayed in treatment longer, but that other factors differed. Women who were married, lived with their spouse, were unemployed, or had higher incomes were more likely to stay in treatment. Black women were less likely to remain in treatment than other women. Among men, employer and family suggestions to enter treatment, abstinence goals, being older, and having fewer psychiatric symptoms predicted treatment retention. In other work, Mammo and Weinbaum (1993) found that women were 31% more likely to drop out of treatment for alcoholism compared to men, and that Black women were at even greater risk of not completing treatment. They also found that women in skilled/semiskilled positions were less likely than their male counterparts to complete treatment. Hser, Polinsky, Maglione, and Anglin, (1999) reported that client matching, based on needs often differentiated by gender (child care, transportation, housing, vocational training), resulted in better retention outcomes. Chou, Hser, & Anglin (1998) found that both men and women remained for shorter periods in treatment settings that received only public funding (compared to public and private funding), and

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men remained in the publicly funded programs for shorter periods than women. In sum, many gender differences exist in the factors that predict whether or not men and women complete treatment. However, despite evidence that men and women have different experiences with treatment initiation and completion, the factors that predict these outcomes have not been reliably established (Annis & Liban, 1980; Walitzer & Connors, 1997). In this report, we examine gender differences in baseline characteristics and treatment process measures for 293 HMO members assessed and recommended for specialty substance abuse treatment. We also present gender-specific models predicting treatment initiation, number of hours spent in treatment, and treatment completion.

2. Materials and methods 2.1. Setting Kaiser Permanente Northwest (KPNW) is a federally qualified HMO that provides comprehensive outpatient and inpatient care to approximately 450,000 members in northwest Oregon and southwest Washington. In general, KPNW members resemble the local area population in age and gender distributions, as well as in health status and other socio-demographic traits (Freeborn & Pope, 1994). 2.2. Substance abuse treatment in KPNW 2.2.1. Benefit structure and co-payments Treatment for substance use problems is covered under a separate benefit structure within KPNW. Except for Medicaid members, outpatient substance abuse treatment services require a small co-payment, as is true for other medical outpatient services. Residential services generally require a 20 percent co-payment and have a 2-year benefit maximum. Most outpatient and other services are provided by the HMO’s Addiction Medicine Department, although a significant minority of clients is recommended for outpatient services with contracted treatment agencies outside the KPNW system. Such recommendations are made: (a) to reduce waiting time; (b) because of convenience of a treatment facility to a member’s place of residence; or (c) in response to special needs (e.g., native language). Residential detoxification is covered as part of the regular medical benefit, and is provided by outside agencies with KPNW Addiction Medicine Department medical staff completing daily rounds. Residential treatment is provided through contracts with outside providers, and KPNW’s Medicaid members’ residential services are covered directly through a state-reimbursed benefit. 2.2.2. Treatment philosophy and structure Clients are assessed and placed into outpatient, intensive outpatient, or residential treatment using criteria similar to

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the patient-placement criteria suggested by the American Society of Addiction Medicine (1996). Alcohol and drug treatment services, whether provided within KPNW or by outside agencies, are abstinence-oriented and include group therapy, individual counseling, and access to gender-specific group sessions. Attendance at meetings of Alcoholics Anonymous, Narcotics Anonymous, or other self-help groups is required, as are random urinalyses. The number of sessions and length of prescribed treatment vary slightly between Oregon and Washington as a result of state accreditation requirements. Most clients are recommended to attend intensive outpatient treatment, which includes four sessions (2.5 hr each) per week for 5 weeks in Oregon or 6 weeks in Washington, followed by up to 10 weeks of continuing care visits. Individual counseling sessions and medical assistance are provided throughout this period as needed; medical assistance is almost always provided through the health plan rather than through outside agencies. 2.2.3. Gender and treatment philosophy Clients seeking treatment are asked if they prefer a male or female case manager, and clinicians may also recommend assignment to same-gender primary case managers for clinical reasons (e.g., history of abuse). During the intensive phase of treatment, patients attend both gender-specific and mixed-gender groups, allowing gender or relational issues to be addressed as they arise. Gender-specific groups for men and women are facilitated by like-gender clinicians, and mixed-gender groups are co-facilitated by female and male clinicians. All continuing care group visits are genderspecific, and when appropriate, women are referred to gender-specific programs to meet their clinical needs (male-only programs do not exist in the metropolitan area served by KPNW). 2.3. Participant recruitment and follow-up For our study, following intake assessment visits, KPNW Addiction Medicine Department counselors offered study participation to adult clients for whom they recommended outpatient substance abuse treatment. Interested clients were referred to independent research staff, who conducted in-person baseline interviews with 102 women and 191 men between July 1998 and March 1999. Approximately 33% of persons assessed during this period were referred to study recruitment staff. Among these, 60% agreed to participate and completed baseline interviews, 12% were not reached for an interview within the 2-week window, 22% were not eligible by study protocol (chiefly, less than 4 months’ prior health plan membership), and 6% declined to participate. If those ineligible by study protocol were excluded from the denominator, the enrollment rate would be 76%. Approximately 7 months following intake assessments, telephone follow-up interviews were completed with 262 (89%) of

the participants. Participants were given gift certificates to a popular ‘‘one-stop’’ store in the amount of $20 for completing the baseline interview and $30 for completing the follow-up interview. Participants were assured that refusing to participate, or dropping out after providing signed consent, would not affect their health plan benefits or substance abuse treatment. At both baseline and follow-up interviews, participants were reminded that they could refuse to answer specific questions if they so desired and that providing accurate information was important. All study methods and procedures were approved and monitored by KPNW’s Institutional Review Board, and all study participants provided informed consent. 2.4. Representativeness of the sample Participants in this study were 102 women and 191 men assessed and recommended for outpatient substance abuse treatment and who had at least 4 months eligibility in the health plan prior to their intake assessment. Participants were a subset of a larger sample (N = 1,472) drawn for a study assessing costs of care following treatment for substance abuse problems. The parent study sample consisted of all health plan members assessed and recommended for substance abuse treatment between April 1, 1998, and March 31, 1999. Administrative and medical records obtained for the larger study provide information that can be used to assess the representativeness of the interviewed sub-sample that is the basis of this report. We compared the interviewed sample with the remaining members of the parent study sample (n = 1,179), using t- and chi-square difference tests, on study variables available for both groups. We detected no statistically significant differences in overall age or in having diagnoses of alcohol problems alone, drug diagnoses alone, or combined alcohol and drug diagnoses. Slightly more members receiving Medicaid participated in the sub-study (19.8% vs. 15.3% in the full sample, p = .06); male sub-study participants were slightly older than men in the parent study (39.8 years vs. 37.4 years, p < .01), and women were slightly more likely to participate in the sub-study than men (34.8% female vs. 29.4% female in the full sample, p = .07). The minor nature of these differences suggests that our sample is representative of persons seeking substance abuse treatment in a large population of HMO members ( > 450,000) that closely resembles the community served by the health plan. Table 1 shows demographic and treatment-related characteristics of the interviewed sample by gender. 2.5. Data sources 2.5.1. Interviews In-person baseline interviews were conducted within 2 weeks of intake assessments and included questions about demographic, social, and substance use characteristics,

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Table 1 Participant characteristics and results of bivariate analyses of baseline and treatment process measures

Baseline measures

Women

Men

Mean (SD) or % n = 102

Mean (SD) or % n = 191

Age (years) 35.87(8.8) 39.84(10.6) Adjusted income $12810.8 $16923.3 (US dollars) ($10483.3) ($11621.6) Number of children 0.9(1.2) 0.6(1.1) living in home Ethnicity identified with most White (non-Hispanic) 80.4% 84.3% Black/African American 12.7% 8.9% Other 6.9% 6.8% Education (highest completed) Grade 11 or less 22.5% 12.6% High school graduate 25.5% 39.3% Technical school/ 37.3% 37.7% some college College graduate or higher 14.7% 10.5% Employed 75.5% 91.1% Living arrangement With family or friends 85.3% 75.4% Alone 10.8 21.5 Controlled environment 3.9 3.1 or no stable housing Married 21.6% 37.7% Medicaid recipient 27.5% 15.7% Used nicotine past 30 days 71.6% 65.4% Veteran 2.0% 30.4% Victim of domestic violence 55.9% 28.3% Victim of forced sex 27.5% 4.7% Religiosity (4-point scale) 2.6(.8) 2.3(.9) Diagnosis Alcohol only 47.1% 52.9% Drug only 30.4% 14.7% Alcohol & Drug 22.5% 32.5% Prior treatment for 49.0% 62.8% alcohol or other drugs Prior counseling 39.2% 24.6% Prior 12-step attendance 29.4% 29.3% weekly for  3-months Reason for seeking treatment Work or school-related 2.0% 7.3% Legal-related 41.2% 44.5% Life out of control 24.5% 14.1% Want to quit 21.6% 22.0% Other 10.8% 12.0% Arrested in past 5 years 51.0% 57.1% In controlled environment 23.5% 26.2% last 30 days Baseline Addiction Severity Index Scores Alcohol .28(.29) .33(.30) Drug .08(.12) .07(.11) Employment .53(.34) .41(.31) Social/Family .24(.26) .14(.19) Legal .15(.22) .16(.21) Medical .32(.33) .27(.32) Psychiatric .34(.27) .25(.24) Days used substance in 30 days before interview alcohol 7.9(9.6) 10.5(10.8) alcohol to intoxication 5.5(8.6) 7.5(10.1) heroin .4(2.8) .4(2.8)

p for t- or c2 difference test .001 .003 .028

n.s.

.032

.0001 .07

.005 .02 n.s. .0001 .0001 .0001 .014 .004

.02 .009 n.s.

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Table 1 (continued )

Baseline measures

Women

Men

Mean (SD) or % n = 102

Mean (SD) or % n = 191

Days used substance in 30 days before interview methadone .60(4.2) .03(0.4) opiates/pain killers .7(3.9) .3(1.4) barbiturates 0.0(0.0) .2(2.2) other sedatives .5(3.1) .4(2.6) cocaine 1.1(3.4) .9(4.1) amphetamines 1.7(5.2) 1.1(4.2) cannabis 2.7(6.9) 2.4(5.9) hallucinogens .1(.5) 0.0(.3) inhalants .3(3.0) .2(2.2) nicotine 20.8(13.3) 18.2(14.1) more than one drug/day 2.9(6.1) 2.9(6.0) SOCRATES Recognition 29.0(6.7) 28.7(6.4) SOCRATES Taking Steps 35.3(4.6) 34.8(5.2) Self-reported health status 2.7(.7) 2.9(.7) Physical/emotional 3.0(1.4) 2.3(1.4) problems interfere with regular activities (5-point scale) Number of 1.9(1.5) 1.2(1.3) medical conditions Number of .4(.8) .2(.5) mental health conditions Number of .2(.4) .1(.4) abuse diagnoses Number of 1.1(.6) 1.2(.8) dependence diagnoses Family history 78.4% 68.1% of AOD problems Referral source Agency/legal 40.2% 40.3% Work-related 3.9% 9.4% Professional 10.8% 7.9% Other 45.1% 42.4% Treatment Process Measures Initiation Hours of treatment Completion

p for t- or c2 difference test n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. .002 .0001

.0001 .009 n.s. n.s. .06

n.s.

79.4% 81.2% n.s. 40.01(60.13) 36.48(32.04) n.s. 41.2% 45.0% n.s.

.10

n.s. n.s.

n.s. n.s. .002 .0001 n.s. n.s. .004 .047 .096 n.s.

adapted from the fifth edition of the Addiction Severity Index (ASI; McLellan et al., 1992). The reference period for most substance use variables was the 30 days prior to the index assessment visit. 2.5.2. Health plan records In addition to interview data, we also abstracted information from Addiction Medicine treatment charts and examined KPNW’s clinical utilization databases for the 6-month period following the intake assessment. The treatment chart provided information on referral to treatment, substance use diagnosis, additional socio-demographic characteristics, history of sexual and physical abuse, military experience, treatment recommendation and location, and treatment discharge. The utilization databases provided

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information on the number and type of substance use treatment visits. 2.6. Dependent variables Dependent variables included measures of treatment initiation, treatment hours, and treatment completion. Neither the assessment visit nor visits for detoxification were considered ‘‘treatment’’ in this study, since detoxification services are provided under the medical benefit rather than the substance-related treatment benefit. Consistent with the strategy of McLellan et al. (1994), we defined treatment initiation as having made two or more treatment visits within the 6 months following the assessment. Members who made one or no visits were coded as not having begun treatment. We defined treatment hours as the estimated total number of hours the client spent in treatment, and calculated it by multiplying each treatment visit type by the usual length (in hours) of that type of visit, summing across all visits in the 6 months following assessment. Individual sessions were counted as lasting 1 hr, intensive outpatient treatment as lasting 2.5 hr, and residential stays (which were rare) as 7 hr of treatment per day. In multivariate analyses, we used a natural log transform (after adding 1 to all values to remove zeros) to normalize its skewed distribution. Treatment completion was determined for most participants from the discharge summary in the treatment chart. When no summary information was available (45% of participants), we defined treatment completion as having made at least 17 outpatient treatment visits in the 6-month period following assessment. This number of visits was based on the expected average number of visits for treatment completion. These measures were based on all treatment visits paid for by KPNW, including those to KPNW substance abuse treatment personnel and those outside KPNW, during the 6 months following the index assessment visit. 2.7. Independent variables 2.7.1. Demographic, social, and health-related characteristics Characteristics collected at the baseline interview included the following: 1. 2. 3. 4.

Age (in years) Gender Race/ethnicity, coded White vs. other, for analyses Education, coded as two binary variables, with ‘‘low education’’ coded 1 for less than high school graduate, and ‘‘high education’’ coded 1 for having education beyond high school graduate — the reference group was therefore participants who graduated from high school)

5. Adjusted income, comprised of total household income for past year divided by number of persons in the household who derived at least half of their economic support from that income 6. Current marital status, coded married or living with partner vs. other 7. Religiosity, a slightly shortened version of the National Opinion Research Center’s General Social Survey 4-point item, coded 1 = not religious at all, 2 = slightly religious, 3 = moderately religious, and 4 = very religious) 8. Employment status, coded employed = 1 vs. other 9. Perceived health status, a 4-point item, coded 1 = poor and 4 = excellent) Missing values on ordinal/continuous measures were replaced with the mean. Missing values for categorical measures were replaced in the most conservative manner possible. For examples, for binary indicators we used negative responses (non-smoker, not married) or the modal response (high school education). Additional information about health status was collected from medical chart review for the year prior to the Addiction Medicine intake assessment. Medical diagnoses abstracted included 22 chronic and acute conditions (excluding pregnancy); mental health diagnoses included disorders in six broad categories (e.g., depressive disorders, personality disorders). In analyses, we included counts of medical diagnoses and counts of mental health diagnoses as indicators of medical and mental health burden, respectively. 2.7.2. Diagnosis, severity of substance use, and related problems As measures of substance-related burden, we counted (separately) number of abuse diagnoses and number of dependence diagnoses recorded in the Addiction Medicine intake assessment. We also included binary indicators of problems with particular drugs (alcohol, cocaine, cannabis, amphetamines, heroin, other opiates) in analyses (0 = absent, 1 = present). Addiction severity and related problems were measured by the 7 composite indices of the ASI. We also included one item indicating the extent to which physical and emotional problems interfered with regular activities during the 30 days prior to assessment (coded on a 5-point scale with 1 = not at all and 5 = extremely). The ASI composite scores have been widely used in treatment studies and measure functioning in 7 domains during a 30-day period: alcohol use, other drug use, employment status, family/social relationships, legal issues, medical status, and psychiatric symptoms (McLellan et al., 1992). Higher values of the composite scores indicate greater problem severity in each domain. For the handful of cases that had missing item-specific data on one of the ASI subscales, the missing value was replaced by the sample mean for the item and the subscale recalculated (as long as 50% or more of the subscale items were present).

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2.7.3. Motivation to change Motivation to change alcohol or drug use was assessed by the 19-item SOCRATES (Stages of Change Readiness And Treatment Eagerness Scale) instrument, Version 8 (Miller & Tonigan, 1996). Differently worded SOCRATES scales were used depending on whether the major problem substance was alcohol or another drug. When participants reported dual alcohol and other drug problems, interviewers asked the participant to choose the version they felt was most relevant. Accordingly, 191 participants completed the alcohol version, and 102 persons completed the otherdrugs version. The SOCRATES was designed to yield three subscales relating to readiness to change alcohol- or drug-related problem behaviors: ‘‘Ambivalence’’ regarding the existence of a problem (4 items), ‘‘Recognition’’ of an alcohol or drug problem (7 items), and ‘‘Taking Steps’’ to ameliorate alcohol or drug-related problems (8 items). However, several items in the Ambivalence subscale were interpreted ambiguously by participants and interviewers, and we chose not to use this scale in our analyses. Reliability coefficients were comparable to those reported by Miller and Tonigan (1996). For the alcohol version, Cronbach’s alphas for Recognition and Taking Steps were 0.92 and 0.86, respectively. For the other-drugs version, the corresponding alphas were comparable at 0.91 and 0.89. Participants were combined in analyses irrespective of the SOCRATES reference substance, so the Recognition and Taking Steps subscales used in analyses refer to alcohol problems for participants identifying alcohol as their primary problem, and to drugs for those indicating a drug or drugs as their primary problem. 2.8. Analysis We tested for bivariate gender differences in baseline measures, treatment initiation, treatment completion, and hours spent in treatment, using t- and chi-square difference tests for continuous and categorical measures, respectively. Following bivariate analyses, we computed gender-specific hierarchical multivariate models, using stepwise procedures. Variables in the models were chosen based on previous studies suggesting factors that predict treatment entry, retention, or outcomes for men, women, or both. Stepwise procedures were used to reduce the number of variables in the final models while still retaining the ability to explore the larger numbers of factors. Blocks were organized and entered based on an ordered conceptual model. Block One included socio-demographic factors (age, employment status, self-reported health status, income, Medicaid status, marital status, education levels, number of children, an indicator of living alone, religiosity). Block Two included measures of health, functioning, and severity of substance-related problems (ASI subscales, arrested in 5 years prior to baseline, in a controlled environment in the 30 days prior to baseline, physical/

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emotional problems interfering with activities in the prior 30 days, prior counseling history, number of medical conditions, number of mental health conditions, number of abuse diagnoses, number of dependence diagnoses, prior 12-step treatment, prior alcohol or other drug (AOD) treatment, referral sources, smoking status, having been a victim of domestic violence, having been a victim of forced sex, armed forces veteran). Block Three included alcohol and drug diagnoses (alcohol, cocaine, cannabis, amphetamine, heroin, opiate). Block Four included motivational factors (SOCRATES Recognition, SOCRATES Taking Steps, entering treatment because life was out of control, entering treatment because wanted to quit using the problem substance). We used logistic regression (likelihood ratio selection, entry criterion p  .05, removal criterion p  .10) procedures to predict treatment initiation and treatment completion. We used a linear regression model (entry criterion p  .05, removal criterion p  .10) to predict the natural log of hours spent in treatment (after adding one to all values to eliminate zeros) to normalize the skewed distribution of this measure. All regression models, whether logistic or linear, were computed using the same block structure.

3. Results 3.1. Gender differences in baseline characteristics As shown in Table 1, men and women recommended for outpatient treatment differed in important baseline characteristics. In comparisons of sociodemographic factors, women were younger than men entering treatment, had lower incomes, had more children living in their homes, were less well educated, were less likely to be employed, were slightly less likely to be living alone, and were less likely to be married. In addition, they were more likely to be Medicaid recipients, less likely to be veterans, and more likely to be victims of domestic violence and forced sex. They also reported being more religious than men. In comparisons of substance-related characteristics, women were more likely than men to have a ‘‘drugonly’’ diagnosis, while men were more likely to have concurrent alcohol and drug diagnoses. Women were less likely to have had prior treatment for alcohol or other drugs but more likely to have had prior counseling. Additionally, women were slightly more likely to report that they sought treatment because their life was ‘‘out of control.’’ In comparisons of social and health characteristics, as measured by the ASI, women were more likely than men to have employment problems, family/social problems, and psychiatric difficulties. Women also reported that their health status was worse and that physical or emotional problems interfered more with their regular activities. Medical chart data supported self-

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reported health status reports — women had more mental and physical health diagnoses than men. Women also were slightly more likely to report having a family history of alcohol or other drug problems.

higher than .05. In the stepped analyses, having less than a high school education and employment status had odds ratios of .368 ( p < .05) and 2.98 ( p < .05), respectively, before marital status entered the equation.

3.2. Gender differences in treatment initiation, completion, and hours of treatment — Bivariate and multivariate analyses

3.2.2. Treatment completion Predictors of treatment completion were also quite different for men and women. Compared to other women, those with higher incomes were more likely to complete treatment, as were women who were referred to treatment for legal reasons or by an agency. Women with more dependence diagnoses and those with worse ASI Employment scores at baseline were less likely to complete treatment than other women. As was true in the above analyses, multicollinearity reduced statistical significance on one factor — number of dependence diagnoses was significant at an alpha of less than .05 before legal/agency referral was added to the equation. After this addition, the alphas for both measures equaled .054. Older men were more likely than younger men to complete treatment, while men receiving Medicaid and those with worse baseline ASI Psychiatric subscale scores were less likely to complete than other men. Multicollinearity between Medicaid members and ASI Psychiatric subscale scores accounts for the alpha of p < .10 for Medicaid recipients. Interestingly, men who reported entering treatment because their life was out of control, as well as those

Women and men did not differ in their likelihood of initiating or completing treatment or in the number of hours they spent in treatment (see Table 1). In multivariate models, however, the factors predicting whether or not men and women initiated and completed treatment differed markedly (see Table 2). 3.2.1. Treatment initiation Women with an alcohol diagnosis were significantly more likely to initiate treatment than other women, while those with more mental health problems were much less likely to begin. For men, socio-demographic factors predicted initiation, although statistical significance was marginal — men who were employed and those who were married were more likely to initiate treatment, while men who had not graduated from high school were less likely initiate treatment. Multicollinearity between these three factors reduced their statistical significance levels to slightly

Table 2 Predictors of treatment initiation and treatment completion (logistic regression analyses), and hours spent in treatment (linear regression analysis)a Initiation Predictors Age Income Medicaid Employed Married Less than high school grad Alcohol diagnosis Opiate diagnosis Baseline ASI psychiatric Baseline ASI employment # MH conditions # of dependence diagnoses Victim of domestic violence Prior 12-step attendance-at least 3 months duration Legal/agency referral source Life out of control Want to quit Goodness of fit R2 b Percent correctly classified a b c d

Women OR (95% CI)

Completion Men OR (95% CI)

Women OR (95% CI)

(Log) Treatment Hours Men OR (95% CI)

Women (beta)

Men (beta)

1.05 (1.01, 1.08)*** 1.07 (1.02, 1.13)** 0.40 (.156, 1.026)*

.204***

3.01 (.99, 9.15)* 2.35 (.99, 5.59)* 0.39 (.148, 1.05)*

.190*** .148**

3.90 (1.38, 11.03)***

.317*** .246** 0.21 (.050, .898)** 0.09 (.014, .526)***

.112 n.s.

0.50 (.26, .94)**

.181*** 0.45 (.203, 1.014)* .207*** .147** 3.23 (.982, 10.59)*

(df = 3) c2 = 1.767 p = .62 .17 81.4

df = 2, c2 = .381 p = .83 .11 82.7

df = 8, c2 = 9.54, p = .33 .33 64.7

.308*** 0.23(.081, .642)*** 0.29(.130, .657)*** df = 8, c2 = 7.105 p = .53 .23 69.1

Blank spaces indicate that the variable did not enter the equation; * = p < .10, ** = p < .05, *** = p < .01. Nagelkerke R2 for logistic regression analyses, multiple R2 for linear regression analyses. Significance of the model = p < .0001. Significance of the model = p < .0001.

n/a

n/a

.20c n/a

.16d n/a

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who entered because they wanted to quit substance use, were less likely to complete treatment. 3.2.3. Hours of treatment Consistent with the patterns described above, factors predicting hours spent in treatment were non-overlapping for men and women. Women with alcohol and opiate diagnoses other than heroin spent more hours in treatment than other women, as did women who reported that their life was out of control. Men receiving Medicaid spent fewer hours in treatment than other men, as did men who did not graduate from high school and men with more mental health conditions. Conversely, married men, men who were victims of domestic violence, and men who had significant prior attendance at 12-step programs spent more hours in treatment than other men.

4. Discussion Despite expectations from previous research that treatment process measures would vary between men and women, we found no significant differences in treatment initiation, treatment completion, or hours spent in treatment. These results provide evidence that gender-sensitive treatment programs may be equalizing the effects of gender differences on key process measures, despite baseline differences in burden of substance abuse and life problems. At the same time, there were numerous gender differences in the factors that predict these process measures, suggesting important, but differential, opportunities for interventions to improve the treatment process for both women and men. Our data indicate that women with alcohol diagnoses are much more likely to initiate treatment and to spend more hours in treatment than women with other diagnoses, but that these women are not more likely to complete treatment. Similarly, having an opiate diagnosis other than heroin predicts spending more hours in treatment for women, but again, does not predict treatment completion. These findings suggest that women with these diagnoses may appear to be more successful during treatment when, in fact, they are no more likely to complete treatment than other women. Clinicians may want to target women with these characteristics in efforts to prevent late treatment dropout. Equally important was the finding that women with more mental health conditions are significantly less likely to initiate treatment. Such women are at great risk of being lost between the initial assessment and the second treatment visit. Special attention to mental health issues among women with multiple mental health problems could increase treatment entry in this group. Additionally, mental health problems also predicted less successful treatment process measures among men — greater ASI Psychiatric subscale score predicted a reduced likelihood of completing treatment. Taken together, these results suggest that mental health problems increase the risk of unsuccessful treatment.

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These findings also imply, however, that assessing mental health-related risks for poor treatment process may be more successful if the number of mental health diagnoses is considered among women, while elevated ASI Psychiatric subscale score is used for men. Since women with more substance dependence diagnoses are also less likely to complete treatment than those with fewer diagnoses, it appears that greater burden of either substance abuse or mental health conditions leads to worse treatment participation among women. The functional consequences of both mental health and substance abuse conditions could reduce both individual capacity to perform treatment role expectations (e.g., attend sessions) and to develop rapport with counselors and fellow clients. It is possible that competing role responsibilities, such as parenting, interact with such functional limitations to affect ability to engage in treatment. Additionally, to the extent that substance abuse evolves from efforts to ‘‘self-medicate’’ among those with mental health difficulties, giving up the substance may be more difficult and may reduce ability to participate in treatment. The ways in which these increased mental health and substance abuse burdens interfere with treatment initiation and completion are not well understood, however, and more research on this issue is needed. Clinicians may also want to target men and women with certain demographic characteristics to increase post-assessment treatment initiation. Men who are not married, those who are not employed, and those who have not graduated from high school are less likely to begin treatment. Men with these characteristics also spend fewer hours in treatment than other men. Similarly, women with lower incomes and those who have employment problems are less likely to complete treatment, as are younger men and men receiving Medicaid. Surprisingly, men who report that their lives are out of control or who enter treatment because they want to quit their problem substance(s) are at greater risk of treatment dropout, suggesting that these motivating factors may not be adequate to sustain men through all phases of treatment. Similarly, SOCRATES scores reflecting treatment motivation did not predict treatment participation measures, contrary to our expectations. 4.1. Conclusions The commonality in these treatment-retention findings is that both men and women with low incomes and mental health problems are less likely to complete treatment. However, the ways these factors are assessed may be important for determining risk — that is, diagnosed mental health conditions may be a better measure for risk among women, while psychiatric symptoms at assessment may be a better measure of risk among men. Additionally, Medicaid status among men appears to be more important than lower income per se, while low income in and of itself appears to reduce likelihood of treatment completion among women. Data on these factors are easily collected during the assess-

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ment process. Future research should determine whether specific efforts designed to help clients manage the difficulties associated with these risk factors could improve treatment initiation and completion, or if targeted motivational enhancement efforts are effective in reducing the impact of these risk factors on clients’ abilities to engage and continue in treatment. Finally, even though we found no differences in proportions of men and women initiating and completing treatment, or in the number of hours spent in treatment, we did identify characteristics that differentially predict these process measures for men and women. Since completing more substance abuse treatment produces better outcomes (Luchansky et al., 2000), these gender-related characteristics, if attended to by clinicians, have the potential to improve treatment initiation and completion, and correspondingly, results of treatment. 4.2. Limitations The primary limitation of this study was its sample size, which may be responsible for marginally significant effect sizes in a few cases. Small sample size may have had an additional impact in situations where there was multicollinearity among the predictor variables. We chose to report several relationships that were significant upon entering the equations but marginally non-significant after a related variable was entered. We made this choice because multicollinearity between predictors was low, r < .20, and we believed that the relationships might remain predictive with larger sample sizes and so should be reported to guide future research. A second limitation is that our indicator of treatment completion was based on estimated hours of treatment for about half of the sample. Although this measure is consistent with the idea of treatment completion used in the program we examined, it may represent more intensive treatment per week for a shorter number of weeks than other outpatient programs. Finally, we drew on the population of a large HMO for this study, which may differ from non-HMO populations. Other research on this population suggests, however, that it closely reflects the community in which it is situated (Freeborn & Pope, 1994), and because it includes both Medicaid and Oregon Health Plan members (generally individuals classified as ‘‘working poor’’ and covered under a waiver to expand Medicaid coverage), likely includes a wider socioeconomic range than do populations of other managed care organizations.

Acknowledgments This study was supported by National Institute on Alcohol Abuse and Alcoholism grant AA10849; Donald K. Freeborn was principal investigator of the project and provided important leadership for this paper. The authors would like to thank the counselors in the Kaiser Permanente

Northwest Addiction Medicine Department and the research participants who made this work possible. We also thank Douglas Brenneman, Stephanie Hertert, Gavin Vilander, Donna White, Ted Trotman, Jewell Ryan, and Elizabeth Shuster for their assistance during various stages of this project. Preliminary analyses were originally reported at the Annual Meeting of the American Public Health Association in Boston (2000).

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