Gender, waitlists, and outcomes for public-sector drug treatment

Journal of Substance Abuse Treatment 25 (2003) 19 – 28

Regular article

Gender, waitlists, and outcomes for public-sector drug treatment Lois Downey, M.A. *, David B. Rosengren, Ph.D., Dennis M. Donovan, Ph.D. Alcohol and Drug Abuse Institute, University of Washington, 1107 NE 45th Street, Suite 120, Seattle, WA 98105, USA Received 7 September 2002; received in revised form 22 March 2003; accepted 22 March 2003

Abstract This study evaluated gender differences in baseline characteristics and treatment outcomes among 654 treatment seekers referred to statefunded drug treatment. Women were significantly less likely than men to enter treatment following referral, but not significantly less likely to complete treatment, once they entered. After adjustment for treatment dose, gender differences in substance use at followup (3 – 6 months after leaving the treatment waitlist) were nonsignificant. The genders did not differ significantly in rates of psychosocial improvement between referral and followup. Women waited significantly longer than men before leaving the treatment waitlist (with or without treatment entry), but wait time was associated with entry rates only among men. The authors discuss system-level and personal characteristics that potentially affect wait times and call for additional study of whether abbreviating waits can increase women’s treatment entry rates. D 2003 Elsevier Inc. All rights reserved. Keywords: Gender; Substance abuse treatment; Treatment entry; Treatment completion; Abstinence

1. Introduction Although substance abuse among women has increased over the past four decades, more men than women abuse drugs and alcohol (Robles et al., 1998). In 1996 women constituted 38% of Americans using illicit drugs (Substance Abuse and Mental Health Services Administration [SAMHSA], 1997) and 29% of those abusing alcohol (SAMHSA, 1998). Washington State estimates from 1994 (Ploeger-Dizon, Beretta, & Krupski, 1997) indicated that 30% of all residents needing substance abuse treatment, and 31% of those at or below the 200% federal poverty line needing treatment, were women. Women are also a minority in substance abuse treatment. Nationwide, women’s representation in treatment appears similar to their representation in the substanceabusing population. In 1996, women constituted 32 – 33% of those in treatment (McCaughrin & Howard, 1996; SAMHSA, 1998, 1999). Among those in treatment, women with alcohol problems have been more likely than their male counterparts to receive treatment in mental health facilities (Weisner & Schmidt, 1992), thus reducing further

* Corresponding author. Box 359927, University of Washington, Seattle, WA 98195, USA. Tel. +1-206-543-8101; fax: +1-206-543-7376. E-mail address: [email protected] (L. Downey). 0740-5472/03/$ – see front matter D 2003 Elsevier Inc. All rights reserved. doi:10.1016/S0740-5472(03)00046-1

their numbers in the formal substance abuse treatment system. These factors, coupled with the fact that men design and run many treatment programs, have led to concerns that the treatment system is geared primarily to a male clientele, with women’s prognoses suffering as a consequence (Nelson-Zlupko, Kauffman, & Dore, 1995). Schneider, Kviz, Isola, and Filstead (1995) offered an unsettling finding in support of this position: women, but not men, in treatment for alcohol problems were more likely to relapse if they completed treatment than if they left treatment before completion. Concerns for women’s treatment outcomes have risen further as research has documented gender-related differences among those in treatment. There is evidence that women in treatment may be younger, less well educated, and more often the recipients of repeated treatment than men (Wechsberg, Craddock, & Hubbard, 1998). They may also have more serious substance-use problems at the point of treatment entry (Kosten, Gawin, Kosten, & Rounsaville, 1993; Weisner & Schmidt, 1992), display higher levels of comorbid psychopathology (Brady, Grice, Dustan, & Randall, 1993; Chatham, Hiller, Rowan-Szal, Joe, & Simpson, 1999; Wallen, 1992; Wechsberg et al., 1998), and have more difficulty reconciling their stereotypes of substance abuse with their own self-images (Thom, 1986). Women seeking alcohol treatment have reported receiving less support for treatment (Beckman & Amaro, 1986), particularly from

20

L. Downey et al. / Journal of Substance Abuse Treatment 25 (2003) 19–28

partners (Thom, 1986, 1987), than men. Thus, researchers have encouraged the addition of women’s programs, women’s groups within mixed programs, and greater sensitivity in the treatment system overall to women’s issues (Hodgins, ElGuebaly, & Addington, 1997; Schliebner, 1994). Despite concerns about women’s treatment outcomes, however, research during the past 15 to 20 years has generally failed to identify gender-related differences in outcome. When differences have been found, they have more often favored women than men. In major substance abuse journals between 1984 and 1989, 17 outcome studies (excluding those investigating nicotine use) included gender evaluations (Toneatto, Sobell, & Sobell, 1992). Of these, 53% found no gender differences in outcome, 41% reported better outcomes for women, and 6% reported better outcomes for men. Research published since that time has generally echoed this pattern. Studies have found women as likely (Fehr, Weinstein, Sterling, & Gottheil, 1991; Johnson, Brems, & Fisher, 1998) or more likely (Williams, Mason, Goldberg, & Cutler, 1996) than men to enter treatment following treatment-seeking attempts. Women have also been as likely (Messina, Wish, & Nemes, 2000; Schneider et al., 1995; Veach, Remley, Kippers, & Sorg, 2000; Wallen, 1992) or more likely (Agosti, Nunes, Stewart, & Quitkin, 1991; Sanchez-Craig, Spivak, & Davila, 1991) to complete treatment. Women’s substance use outcomes have been as good as (Alterman, Randall, & McLellan, 2000; Chatham et al., 1999; McCance-Katz, Carroll, & Rounsaville, 1999; Messina et al., 2000; Mulvaney et al., 1999) or better than (Kosten et al., 1993; Project MATCH Research Group, 1997; Sanchez-Craig et al., 1991; Weiss, Martinez-Raga, Griffin, Greenfield, & Hufford, 1997) men’s, and women have shown greater psychosocial improvement following treatment than men (Chatham et al., 1999; Weiss et al., 1997). However, many mixed-gender studies do not report gender-specific outcomes. In the non-nicotine studies reviewed by Toneatto et al. (1992), 44 included participants of both genders, but only 17 of these (39%) reported having evaluated outcomes by gender, and even fewer reported on specific outcomes. Only 5 studies (11%) reported dropout rates and only 7 (16%) reported subsequent treatment outcomes by gender. Researchers have speculated that the failure of many mixed-gender studies to report genderspecific outcomes results from small sample sizes for women and resulting lack of power for discerning genderrelated differences (e.g., Moras, 1998). Indeed, recent studies by Arfken and colleagues (Arfken, Borisova, Klein, di Menza, & Schuster, 2002; Arfken, Klein, di Menza, & Schuster, 2001), based on a large administrative dataset from Detroit, reported that at least among persons seeking publicly funded treatment, women’s outcomes may be worse than those of men. These authors evaluated outcomes across genders and treatment settings among persons qualifying for publicly funded treatment in the middle to late 1990s. Women showed lower treatment

entry rates within 30 days of assessment than did men, and women entering treatment were less likely to complete their treatment programs than were men. Analyses included adjustments for factors such as demographic risk characteristics, primary drug, problem severity at initial assessment, treatment setting, and treatment priority. The present article represents an extension of the investigations by Arfken et al. (2001, 2002). Like the Detroit studies, ours was based on information about persons referred to publicly funded treatment through a centralized intake unit (CIU) and investigates gender differences on several pre- and post-treatment variables. However, our study is based in a different geographic area (Seattle), uses a sample with substantially different racial composition (37% African-American, compared to 85% in the Detroit sample), examines treatment entry rates past the 30-day window used in the Detroit studies, and includes not only administrative data on treatment entry and completion, but also data on longer-term outcomes (substance use and psychosocial adjustment during a followup period) collected directly from the study sample. We investigated four questions: 

Are women drug abusers as likely as their male counterparts to seek treatment?  How do men and women differ at the point of treatment seeking?  Is gender associated with treatment entry, completion, or posttreatment outcomes?  What factors may have contributed to gender differences in outcome?

2. Materials and methods 2.1. Study setting This study originated in a 1995 –1996 investigation of methods to improve treatment entry rates and outcomes for persons referred to treatment through Washington State’s Alcoholism and Drug Addiction Treatment and Support Act (ADATSA). The study was approved by the University of Washington Human Subjects committee. Enrollees provided informed consent. We recruited study participants following their formal evaluation and referral for ADATSA-funded treatment at a CIU that served as the sole gateway to publicly funded treatment for residents of the Seattle-King County area. ADATSA funding was available for public assistance recipients who were incapacitated and unemployed by reason of chemical dependency, actively addicted, amenable to available treatments, and seeking a treatment plan other than methadone maintenance. Pregnant women, whose treatment was covered by an alternate funding source, were ineligible for study participation. In addition to ADATSA eligibility, study participants were required to be actively addicted to

L. Downey et al. / Journal of Substance Abuse Treatment 25 (2003) 19–28

an illicit drug (i.e., not solely alcohol-dependent), to be applying for a new treatment cycle, and to speak English. All CIU clients had to meet specific pretreatment requirements. Those clients referred to outpatient treatment received an intake appointment time and date during their initial CIU evaluation. Their sole responsibility was to present for the scheduled treatment intake. Those referred to inpatient or residential programs did not obtain specific referrals at the time of their initial evaluation, but were required to phone the CIU placement office at least weekly to learn of appropriate treatment openings, and to accept a treatment slot when offered. Removal from the CIU treatment waitlist could occur for any of the following reasons: treatment admission, failure to present for a scheduled admission, refusal to accept an available treatment slot, client request for removal, or failure to make a check-in call to the placement office for 2 weeks or more. Study enrollment typically happened on the same day as the CIU evaluation. A baseline study assessment occurred as soon as possible after study enrollment, and always before participants left the treatment waitlist. Followup assessment occurred 90 to 180 days following removal from the waitlist. Ninety percent of study enrollees completed followup assessments. 2.2. Measures The baseline assessment elicited participants’ selfreports concerning several factors found to differ by gender in other research on treatment seekers. These included age, years of education, and receipt of previous substance abuse treatment. Three instruments measured baseline severity of problems: (a) the minimum estimated days (1 –90) on which participants used illicit drugs or drank heavily during the 90 days before enrollment (Sobell, Kwan & Sobell, 1995); (b) the standard composite InDUC score (0– 135, 0 = no negative consequences) from the Inventory of Drug Use Consequences (Miller, Tonigan, & Longabaugh, 1995); and (c) the average ANH life problems score (0– 4 = no problems to extreme problems) from the Areas in Need of Help Questionnaire (Power, Hartnoll, & Chalmers, 1992). Two instruments measured comorbid psychopathology: (a) the psychiatric severity subscale (0 – 1, 0 = no psychiatric issues) from the Addiction Severity Index (McLellan et al., 1992); and (b) the SCL-21 distress score (21 –84, 21 = no distress symptoms) from the 21-item Symptom Checklist (Green, Walkey, McCormick, & Taylor, 1988). We evaluated social support with a study-specific adaptation of the Important People and Activities questionnaire (Clifford & Longabaugh, 1991), from which we computed four scores: the number of people (0 –5) who supported the enrollee’s treatment attempt, the number (0 –5) who supported abstinence, and two dichotomous measures indicating whether the participant had a primary partner who represented a barrier to treatment or abstinence.

21

Six measures in this study were factors the research team’s prior investigations (Downey, Rosengren, & Donovan, 2000b; Downey, Rosengren, Jackson, & Donovan, 2003; Hansten, Downey, Rosengren, & Donovan, 2000) found to be associated with treatment outcomes: drug of choice, confidence in treatment entry, readiness to change, self-concept motivation for abstinence, referral type, and length of wait for treatment. Participants indicated drug of choice as an open-ended response; we recoded it into two categories: heroin and other drugs. Confidence in treatment entry was a single item coded 0 – 4 (0 = no confidence). Readiness to change was measured with the taking-steps score (8 –40, 8 = not ready to change) from the Stages of Change Readiness and Treatment Eagerness Scale (Miller & Tonigan, 1996). The Reasons for Quitting Questionnaire (Downey, Rosengren, & Donovan, 2000a; McBride et al., 1994) provided a measure of self-concept-related motivation for abstinence (0 – 4, 0 = no motivation for abstinence related to discrepancies between substance use and selfconcept). From CIU client charts we extracted referral type (inpatient vs. outpatient) and computed the length of wait for treatment. For participants who left the waitlist through treatment entry, failed to appear for a scheduled admission, refused an available treatment slot, or made an explicit request for removal, wait time was the number of days between CIU evaluation date and waitlist removal. For participants removed because of failure to maintain regular contact with the CIU, we used the day midway between the last recorded contact and the official file closure (approximately 2 weeks after the last contact). We defined 4 ordinal levels of treatment dose (no treatment, left treatment without completing, completed treatment, and still in treatment at time of followup); these data usually came from CIU charts, but for participants who entered alternative treatment (i.e., not through CIU referral), the data came from self-reports at the followup assessment. We considered six outcome measures. From CIU client charts we extracted dichotomous measures indicating whether participants entered and completed treatment. At the followup assessment, participants reported their days of substance use during the 90 days prior to assessment (comparable to the baseline measure); this report produced two outcome measures: days of use and a dichotomous variable reflecting abstinence or nonabstinence for the entire 90-day period. The followup assessment also included the ANH and SCL-21 questionnaires, from which we computed the ANH life-problems and SCL-21 distress scores as at baseline; from each of these scores we subtracted the corresponding baseline score (life problems = 4 to + 4; psychological distress = 63 to + 63). Negative change scores reflected reduced problems. At the followup assessment, participants reported how many days during the followup period they had been incarcerated or hospitalized for medical or psychiatric care. We subtracted this number from 90 to provide an estimate of the days on which they were vulnerable for substance use, a

22

L. Downey et al. / Journal of Substance Abuse Treatment 25 (2003) 19–28

variable that served as the rate multiplier for Poisson regression models. 2.3. Statistical analysis We evaluated gender differences on pretreatment characteristics (Table 1) with Pearson chi-square, Fisher’s exact test, and the Mann-Whitney/Wilcoxon rank sum test. Logistic regression models tested for associations between gender and dichotomous outcomes. For each model we report the estimated odds ratios (OR), Wald’s 95% confidence intervals (CI), and probabilities ( p) based on the likelihood ratio test. We used Poisson regression with probabilities corrected for over-dispersion (Gardner, Mulvey, & Shaw, 1995) to test for associations between gender and days of substance use during the followup period. For these tests, we report rate ratios (RR), in addition to the confidence intervals and probabilities. After testing multiplicative logistic and Poisson models, we performed further analyses to test for statistically significant interactions between gender and other predictors of outcome. In secondary investigations of the association between wait time and treatment entry status, we employed Cox proportional hazards regression models; for these we report hazard ratios (HR). We used Wilcoxon signed ranks tests Table 1 Sample characteristics by gender Categorical Variables Racial Category Caucasian African-American American Indian Other Has Partner Who Supports Use Has Partner Who Doesn’t Support Treatment Received Previous Treatment Heroin as Drug of Choice Outpatient Referral

Women % of 206

Men % of 448

p .069

40 42 11 7

51 34 10 5

20

15

.114

7 65 20 13

4 70 18 16

.077 .207 .590 .350

Rank-Order Variables

Mean

Mean

p

Age Years of Education Days of Substance Use Negative Consequences (InDUC) Severity of Life Problems (ANH) Psychopathology (ASI) Psychiatric Distress (SCL) # People supporting treatment # People opposing use Self-concept-related abstinence motivation Confidence in treatment entry Taking steps (SOCRATES) Days of wait for treatment

33.04 11.81 58.67 51.21 2.13 0.41 41.74 3.52 3.66

34.70 11.91 59.14 51.77 2.04 0.35 39.64 2.99 3.27

.016 .341 .811 .654 .097 .002 .014 .000 .002

3.49 3.79 33.19 29.36

3.33 3.77 31.92 16.31

.015 .913 .001 .000

to evaluate within-gender change in psychosocial adjustment between baseline and followup, and Mann-Whitney/ Wilcoxon rank sum tests to investigate gender associations with change scores. We based probabilities on nondirectional hypotheses. In evaluating gender differences on pretreatment variables (Table 1), we used a Bonferroni adjustment for 19 tests, requiring p < .003 for statistical significance, for an overall a < .05. In considering gender associations with outcomes, we reserved the term ‘‘significant’’ for results associated with p < .001.

3. Results 3.1. Study participation and followup rates by gender Between September 1, 1995, and August 31, 1996, CIU case monitors found 1,379 treatment seekers (449 women and 930 men) to meet eligibility qualifications for the study. The gender distribution of these welfare recipients seeking treatment (33% female) closely approximated the estimated gender distribution (31% female) among the Washington state poor needing substance abuse treatment during the study period (Ploeger-Dizon et al., 1997). This suggests that women from this population who needed treatment were as likely as men to seek it. Of 1,379 treatment seekers eligible for the study, 654 enrolled and provided reliable baseline data. This sample included 206 (46%) of the eligible women and 448 (48%) of the eligible men. Among eligible women who did not participate, case monitors failed to inform 21 (9%) about the study; 91 (37%) had no interest in participation, 128 (53%) did not complete the baseline study assessment, and 3 (1%) provided unreliable baseline data. Among eligible men not in the study sample, 83 (17%) were not informed about the study, 212 (44%) were not interested, 182 (38%) lacked complete baseline assessment, and 5(1%) provided unreliable baseline data. Although overall participation rates did not differ by gender (Fisher’s exact p = .454), there was a significant gender difference in the timing of attrition (m2[3] = 18.4, p = .000), with women more likely than men to express an interest in the study but drop out before completion of the baseline assessment. Completion of the followup assessment was unrelated to gender. Ninety percent of men and 89% of women in the sample completed the followup (Fisher’s exact p = .582). 3.2. Sample characteristics by gender Table 1 summarizes the baseline characteristics of men and women in the study sample. The two groups did not differ significantly in racial composition or on several characteristics prior researchers found to be associated with gender: age, education, previous treatment, or seriousness of substance use problems (days of use, negative consequen-

L. Downey et al. / Journal of Substance Abuse Treatment 25 (2003) 19–28

ces, or severity of life problems) at the point of treatment seeking. Contrary to findings reported in earlier alcohol research, women in this sample reported significantly greater support for treatment and significantly greater opposition to their substance use from their personal networks than did men, although the two gender groups were equally likely to report having primary partners who created barriers to behavior change. Consistent with reports from other researchers, women reported significantly higher levels of psychopathology than men, based on the Addiction Severity Index (ASI), although gender differences on SCL distress symptoms were nonsignificant. Previously, we found six pretreatment variables predicted outcomes in the total sample (Downey et al., 2000b, 2003; Hansten et al., 2000). Of these six, women and men differed significantly on only two. They did not differ in drug of choice (heroin vs. other drugs), confidence in treatment entry, being referred to outpatient rather than inpatient treatment, or abstinence motivation linked to difficulties in reconciling substance use with self-concept. Women had significantly higher scores on Stages of Change Readiness and Treatment Eagerness Scale (SOCRATES) taking steps, suggesting greater readiness for change and potentially better outcomes. However, they also waited significantly longer than men for treatment, a factor negatively associated with treatment entry in the sample as a whole. Among treatment entrants, women waited over twice as long as men (means of 29.64 and 14.00 days, respectively; p = .000), with smaller differences for nonentrants (28.96 and 23.65 days, p = .017). Inpatient/residential waits were 31.94 days for women and 17.70 days for men ( p = .000); outpatient waits were 12.26 and 9.04 days, respectively ( p = .204). Participants were dropped from the waitlist without treatment entry for the following reasons: client request for removal (9.5% of women, 11.2% of men), failure to maintain contact with the CIU (66.7%, 62.6%), no-show for treatment (21.4%, 23.4%), premature detoxification departure (2.4%, 1.9%), and incarceration (0.0%, 0.9%). The distribution of reasons for removal did not differ significantly by gender (m2[4] = 1.17, p = .883). 3.3. Gender differences in treatment outcomes 3.3.1. Treatment entry Significantly fewer women (59%) than men (76%) entered the treatment programs to which they were referred (m2[1] = 19.48, p = .000). A multivariate logistic regression model (Table 2) revealed both a significant main effect of gender and a significant interaction between gender and wait time in predicting this outcome. The result demonstrated that women were less likely to enter treatment than men, and that wait time, which had a strong negative association with treatment entry among men, had a weaker association among women. Interactions between gender and other predictors of treatment entry were nonsignificant.

23

Table 2 Logistic regression model of treatment entry Predictors

ORa

95% CIb

pc

Female Wait timed Female by wait time interaction Outpatient referral Confidence in treatment entrye Heroin as drug of choice

0.19 0.97 1.04 5.47 2.05 0.42

0.10 – 0.37 0.95 – 0.99 1.01 – 1.07 2.45 – 12.22 1.51 – 2.78 0.26 – 0.65

< .001 < .001 < .001 < .001 < .001 < .001

a

Estimated odds ratio. 95% confidence interval based on Wald’s test. c Probability based on the likelihood ratio test. d Days in wait period, entered as a linear predictor; nonsignificant departure from linear trend. e Rank order variable (0 – 4), entered as a linear predictor; nonsignificant departure from linear trend. b

3.3.2. Treatment completion Among the 336 men and 117 women who entered treatment, completion rates for the two gender groups were virtually identical (71% and 70%; Fisher’s exact p = .815). There was no evidence of a gender effect that was masked by confounding variables. The variable most strongly associated with treatment completion, referral type, was not associated with gender. In a multiplicative model, adjusted for referral type, the OR for treatment entry among females was 0.95 (CI = 0.59 – 1.52, p > .82). 3.3.3. Substance use during the followup period Women were considerably more likely than men to report some illicit drug use and heavy drinking during the 90-day followup period (63% vs. 52%, Fisher’s exact p = .012), but much of the difference was related to women’s lower treatment entry rates. In a multivariate model adjusted for the two strongest predictors of the dichotomous substance use outcome — treatment dose and self-concept-related motivation — the gender effect was nonsignificant, with the OR for females = 1.46 (CI = 0.98 – 2.17, p > .06). Interactions between gender, treatment dose (four ordinal levels), and self-concept motivation were nonsignificant. Similarly, women reported more substance use days during the followup period than did men: 21.46 vs. 14.89 days of use ( p based on the Wilcoxon/Mann Whitney test = .002). However, as with the abstinence outcome, this association was confounded by treatment dose. With vulnerability for use as the rate multiplier, the multivariate Poisson model adjusted for treatment dose yielded RR = 1.08 (CI = 0.82– 1.41, p > .59) for females. 3.3.4. Psychosocial adjustment Both men and women reported significant improvements in psychosocial functioning between enrollment and followup. Men’s mean life problems decreased from 2.06 to 1.16 ( p = .000); women’s, from 2.11 to 1.35 ( p = .000). The slightly smaller mean change for women than for men ( 0.77 vs. 0.90) was nonsignificant ( p = .072). Similarly,

24

L. Downey et al. / Journal of Substance Abuse Treatment 25 (2003) 19–28

there were significant decreases in psychological distress (from 39.98 to 33.61 for males, p = .000; from 41.72 to 36.59 for females, p = .000). The mean decreases for women and men were not significantly different ( 5.13 and 6.37; p = .098). 3.4. Factors associated with gender differences in treatment entry Although the logistic regression model exposed a significant interaction between gender and wait time in predicting treatment entry, additional analyses were necessary for a more thorough understanding of gender differences. First,

logistic regression models within the gender-specific strata showed wait time to have almost no effect on entry rates among women, but a strong negative effect among men. For each gender we ran a multivariate model that included referral type, confidence in treatment entry, heroin preference, and wait time — the four significant predictors of treatment entry in the full sample. In the analysis of women, the odds ratio for wait time was nonsignificant (OR = 1.01, CI = 0.99 – 1.03, p > .27). There were two significant predictors of treatment entry for women: referral to outpatient treatment (OR = 22.07, CI = 4.29 – 113.35, p < .001) and heroin preference (OR = 0.21, CI = 0.09 – 0.50, p < .001). The remaining predictor, confidence in treatment

Fig. 1. Treatment entry status by wait day.

L. Downey et al. / Journal of Substance Abuse Treatment 25 (2003) 19–28

entry, had a nonsignificant association (OR = 1.96, CI = 1.08 –3.56, p < .02). By contrast, in the four-predictor model for men, there was a significant negative association with length of wait (OR = 0.97, CI = 0.95– 0.99, p < .001). Confidence in entry (OR = 2.14, CI = 1.49 – 3.05, p < .001) was also a significant predictor for men, with the remaining predictors nonsignificant: referral to outpatient treatment (OR = 2.73, CI = 1.11– 6.67, p < .02), and preference for heroin (OR = 0.55, CI = 0.31 – 0.96, p < .04). Second, Cox models, adjusted for drug of choice, referral type, and confidence in treatment entry, demonstrated that the primary wait time-related gender effect was a differential rate of treatment entry, not a differential rate of waitlist attrition. The Cox model using treatment entry as the outcome, and censoring cases who left the waitlist without treatment entry, revealed a significant effect of gender (HR for females = 0.41, CI = 0.33 – 0.51, p < .001). By contrast, in the Cox model using attrition as the outcome and censoring cases as they entered treatment, gender was nonsignificant (HR for females = 0.85, CI = 0.63 –1.15, p > .29). Among persons still on the waitlist at any time point, women had less than half men’s odds of entering treatment, but no greater odds of dropping off the waitlist without treatment entry. Fig. 1 provides a graphic demonstration of the differences, by gender and over time, between treatment entry and waitlist attrition. For each day during the wait period, the graphs show the proportion of each gender who had entered treatment by that day (bottom portion of the graph), remained on the waitlist (middle portion), or had dropped off the waitlist without treatment entry (top portion). The male and female graphs differ primarily during the first 21 days of wait, when men entered treatment at high rates, while women continued to wait. By day 21, over 63% of the men, but fewer than 29% of the women, had entered treatment. During the first 21 days, women did not differ significantly from men in their rates of waitlist attrition (14% and 11%, respectively; Fisher’s exact p = .246). Remaining on the waitlist were 57% of the women, compared with 26% of the men. Among those with wait times longer than 21 days, women and men entered treatment at similar rates (53% and 50%, respectively, Fisher’s exact p = .601).

4. Discussion This study was conducted with a large sample (n = 654) of persons seeking publicly funded drug treatment through a CIU and included almost half of the study-eligible treatment seekers in the Seattle area during the recruitment period. The representation of women in the eligible group closely approximated the representation of women among those below the poverty line in Washington State believed to need substance abuse treatment (Ploeger-Dizon et al., 1997). This suggests that women and men needing treatment likely

25

sought treatment at similar rates. Moreover, women constituted 31% of the final study sample and were present in adequate numbers (n = 206) to allow evaluation of gender differences vis-a`-vis study outcomes. In general, results of this study were consistent with findings from other research suggesting that women fare about as well as men in formal substance abuse treatment. However, for one outcome — treatment entry following referral — women’s outcomes were significantly worse than men’s. This replicates a finding by Arfken and colleagues (2002), reported for a primarily African-American sample from the Midwest and an observation period extending 30 days after treatment-seekers’ evaluation for treatment. Our study, conducted on the West coast with a more balanced mix of Caucasians and African-Americans, and using a longer observation period, produced an even stronger gender association with treatment entry (59% and 76% entry rates for women and men, respectively) than did the Arfken study (72% and 79%). Gender difference in our sample occurred primarily during the first 21 days after referral. The two studies, taken together, suggest that at least among the urban poor, women are less likely than men to enter treatment in the weeks immediately after referral, and that this phenomenon is not peculiar to one geographic area. We had previously identified four factors associated with treatment rates in this sample (Downey et al., 2003; Hansten et al., 2000). Men and women did not differ significantly on three of them: referral type (inpatient/outpatient), primary heroin use, and confidence in treatment entry. However, there was a significant gender difference on one factor: wait time. Women waited slightly longer than men for outpatient treatment, a pattern that replicates findings from a national survey of outpatient programs (McCaughrin & Howard, 1996), and significantly longer than men for admission to inpatient and residential programs, which constituted the vast majority of referrals in our sample. Other studies have noted an association between wait time and treatment entry (e.g., Fehr et al., 1991; Stark, Campbell, & Brinkerhoff, 1990), although the finding is not universal (Alterman, Bedrick, Howden, & Maany, 1994). Participants in our study cited long waits for CIU evaluation and subsequent treatment slots as important reasons for a declining interest in pursuing treatment. Data collected from this study sample provided almost no clues about the causes of women’s longer wait times. We must base our conjectures on supplementary and anecdotal evidence. Two system factors appear to have contributed. First, our study excluded pregnant women — a high-priority group that was offered treatment immediately following CIU evaluation, rather than being placed on waitlist status. Female inpatient slots that would otherwise have been available to ADATSA applicants in our study on the basis of waitlist seniority would always have been offered first to any pregnant women awaiting treatment. An independent search of Washington State administrative data revealed that during the period of study recruitment, 112 pregnant women

26

L. Downey et al. / Journal of Substance Abuse Treatment 25 (2003) 19–28

were evaluated and referred to treatment by the CIU, and of these, 65 were admitted to treatment within 1 year of evaluation. To the extent that these 65 women entered treatment immediately following referral, women in our sample waited longer, thus explaining part of the gender disparity in wait times. Anecdotal evidence suggests a second system factor that may have contributed to longer waits for women. According to CIU placement staff, women in general — not just ADATSA-funded women — waited longer than men for inpatient beds. In Washington State during the period of this study, each treatment program controlled how many of its available inpatient slots were offered to applicants of each gender. There were no external mandates regarding allocations by gender. These decisions were driven primarily by financial considerations aimed at keeping facilities functioning at or near capacity, and by security-linked considerations. Most programs could, and did, change the male-to-female ratio of treatment slots frequently and without difficulty in response to demand. However, large facilities with multi-residence campuses were less flexible. The program that served the majority of inpatient referrals in this study (77% of inpatient treatment entrants) operated a campus of this sort. For security reasons, women and men resided in separate buildings. Increasing the number of slots available to women would have required a 50 – 100% increase in the female census. Such a change, if made permanently, would have resulted in less-than-capacity operation, an untenable fiscal position in an era of increased vigilance and accountability for use of public funds. Temporary changes were logistically difficult for a facility of this size to implement and thus were infrequent. Other anecdotal evidence suggests that women’s waits may have been lengthened because of family responsibilities. The CIU placement staff indicated that women were more likely than men to call the office shortly before scheduled treatment entry to report an inability to arrange childcare and to ask for postponed treatment. The placement staff typically accommodated these requests. In such cases, it was the lack of childcare services, rather than the unavailability of beds, that slowed women’s entry into treatment. Besides requests for delayed entry, women were more likely than men to request treatment in specific facilities near their residences, in order to allow family visitation. Honoring such requests reduced flexibility in placement, indirectly lengthening waits. Evidence exists, however, that women’s entry rates may remain low even if gender disparities in wait time are eliminated. Outpatient referral (a treatment allowing more flexibility in meeting family responsibilities) — not wait time — predicted treatment entry for women in our study. Although dramatic reductions in wait time might have produced an association between wait time and entry, pregnant women, offered immediate treatment, entered at virtually the same rate (58%) as women in our study (59%; Fisher’s exact p = .905). The fact that women, more often

than men, indicated an interest in our study, but failed to follow through with enrollment, provides additional indirect evidence that factors specific to the women themselves, rather than those arising from a sluggish treatment system, interfered with motivation to enter treatment. Similarly, Arfken and colleagues (2002) reported disproportionate early dropout among women (before completion of CIU intake), which was likely not due to lengthy waits for treatment beds. Our data did not support the finding reported by Arfken and colleagues (2001) of a gender effect on treatment completion. Completion rates in our study (70% for female entrants and 71% for male) were substantially higher than those in the Detroit study (24% and 46%), and the gender difference was not statistically significant. However, review of the supplementary data from pregnant women evaluated at the CIU suggests that female treatment completion rates in our study may have been inflated by the exclusion of pregnant women. Among the 65 pregnant women who entered treatment within 1 year of referral, under 25% completed it. Had we recruited 46% of the CIU’s pregnant women for our study, and had their treatment entry and completion rates reflected those of the pregnant women overall, our female completion rate would have dropped to less than 61% — producing a Fisher’s exact p of .026 when compared to the rate for men. As with most research, our study has limitations. Most importantly, the participants consisted exclusively of persons referred to ADATSA-funded drug treatment in King County, Washington, in 1995– 1996. Although the extent to which our results generalize to other drug using, treatmentseeking populations, other geographic locations, and other time periods is unknown, the recent reports from Arfken et al. (2001, 2002) suggest that the association of gender with treatment entry was not geographically idiosyncratic. To the extent that the observed disparity in entry rates reflected women’s complicated familial roles and responsibilities (factors that other researchers have noted), the phenomenon extends beyond our study population. It is important to note that, in the interest of reducing the likelihood of Type I error, we adopted very conservative criteria for detecting statistically significant patterns. This may have led us to disregard some factors that are actually associated with treatment entry within gender groups, as well as some factors on which lower-income male and female treatment seekers differ. Investigation of these issues with larger samples, offering greater power, is warranted. Our results suggest good, bad, and uncertain news for women. Women’s treatment completion and post-treatment success rates were comparable to those of men, despite the facts that almost no women were referred to female-only programs, and that many of the mixed-gender facilities did not make special provision for women’s treatment needs. Although almost no women entered treatment quickly, they were at least as able as men to endure lengthy waits when required to do so. Among those waiting more than 3 weeks

L. Downey et al. / Journal of Substance Abuse Treatment 25 (2003) 19–28

after referral, women were as likely as men to enter treatment. However, women were less likely than men overall to enter treatment, and as a result of their lower treatment rates, they experienced fewer successful substance use outcomes. It appears unlikely that shorter waits alone would increase women’s treatment rates. The uncertainty involves what additional resources treatment programs might offer to women, and at what cost, and whether services that allow women to meet familial responsibilities concurrent with treatment would increase treatment rates. Empirical investigation of these matters will provide useful data for treatment policy planning.

Acknowledgments Research for this article was supported by grant #DA08751 from the National Institute on Drug Abuse. The authors thank Toni Krupski, Edie Henderson, and Kevin Campbell (Division of Alcohol and Substance Abuse, Washington State Department of Social and Health Services), Pat Ehrhardt (Placement Office, King County Assessment Center) and Pamela W. Miles (University of Washington, Alcohol and Drug Abuse Institute) for background information used in this article. We are also grateful for the contributions of study staff (Sue Brennan, Lonn Friese, Bryan Hartzler, Michelle Hansten, Nicole Hasenberg, Jennifer Neill, Sorrel Stielstra, and Midge StrongBeers), the investigator team (Gary Cox, Kevin Sloan, Steven Freng, Judith M. Matson and Mike Elsner), the King County Assessment Center staff, and study participants.

References Agosti, V., Nunes, E., Stewart, J. W., & Quitkin, F. M. (1991). Patient factors related to early attrition from an outpatient cocaine research clinic: A preliminary report. The International Journal of the Addictions, 26, 327 – 334. Alterman, A. I., Bedrick, J., Howden, D., & Maany, I. (1994). Reducing waiting time for substance abuse treatment does not reduce attrition. Journal of Substance Abuse, 6, 325 – 332. Alterman, A. I., Randall, M., & McLellan, A. T. (2000). Comparison of outcomes by gender and for fee-for-service versus managed care: A study of nine community programs. Journal of Substance Abuse Treatment, 19, 127 – 134. Arfken, C. L., Borisova, N., Klein, C., di Menza, S., & Schuster, C. R. (2002). Women are less likely to be admitted to substance abuse treatment within 30 days of assessment. Journal of Psychoactive Drugs, 34, 33 – 37. Arfken, C. L., Klein, C., di Menza, S., & Schuster, C. R. (2001). Gender differences in problem severity at assessment and treatment retention. Journal of Substance Abuse Treatment, 20, 53 – 57. Beckman, L. J., & Amaro, H. (1986). Personal and social difficulties faced by women and men entering alcoholism treatment. Journal of Studies on Alcohol, 47, 135 – 145. Brady, K. T., Grice, D. E., Dustan, L., & Randall, C. (1993). Gender differences in substance use disorders. American Journal of Psychiatry, 150, 1707 – 1711. Chatham, L. R., Hiller, M. L., Rowan-Szal, G. A., Joe, G. W., & Simpson,

27

D. D. (1999). Gender differences at admission and follow-up in a sample of methadone maintenance clients. Substance Use & Misuse, 34, 1137 – 1165. Clifford, P. R., & Longabaugh, R. (1991). Manual for the Important People and Activities Instrument. Providence, RI, Brown University: Center for Alcohol and Addiction Studies. Downey, L., Rosengren, D. B., & Donovan, D. M. (2000a). Sources of motivation for abstinence: A replication analysis of the Reasons for Quitting questionnaire. Addictive Behaviors, 26, 79 – 89. Downey, L., Rosengren, D. B., & Donovan, D. M. (2000b). To thine own self be true: Self-concept and motivation for abstinence among substance abusers. Addictive Behaviors, 25, 743 – 757. Downey, L., Rosengren, D. B., Jackson, T. R., & Donovan, D. M. (2003). Primary heroin users + drug-free treatment: An equation for success? Addictive Behaviors, 28, 339 – 346. Fehr, B. J., Weinstein, S. P., Sterling, R. C., & Gottheil, E. (1991). ‘‘As soon as possible’’: An initial treatment engagement strategy. Substance Abuse, 12, 183 – 189. Gardner, W., Mulvey, E. P., & Shaw, E. C. (1995). Regression analyses of counts and rates: Poisson, overdispersed Poisson, and negative binomial models. Psychological Bulletin, 118, 392 – 404. Green, D. E., Walkey, F. H., McCormick, I. A., & Taylor, A. J. (1988). Development and evaluation of a 21-item version of the Hopkins Symptom Checklist with New Zealand and United States respondents. Australian Journal of Psychology, 40, 61 – 70. Hansten, M., Downey, L., Rosengren, D. B., & Donovan, D. M. (2000). Relationship between follow-up rates and treatment outcomes in substance abuse research: More is better but when is ‘‘enough’’ enough? Addiction, 95, 1403 – 1416. Hodgins, D. C., El-Guebaly, N., & Addington, J. (1997). Treatment of substance abusers: Single or mixed gender programs? Addiction, 92, 805 – 812. Johnson, M. E., Brems, C., & Fisher, D. G. (1998). Unmet treatment needs of drug users in Alaska: Correlates and societal costs. International Journal of Circumpolar Health, 57 (Suppl 1), 467 – 473. Kosten, T. A., Gawin, F. H., Kosten, T. R., & Rounsaville, B. J. (1993). Gender differences in cocaine use and treatment response. Journal of Substance Abuse Treatment, 10, 63 – 66. McBride, C. M., Curry, S. J., Stephens, R. S., Wells, E. A., Roffman, R. A., & Hawkins, J. D. (1994). Intrinsic and extrinsic motivation for change in cigarette smokers, marijuana smokers, and cocaine users. Psychology of Addictive Behaviors, 8, 243 – 250. McCance-Katz, E. F., Carroll, K. M., & Rounsaville, B. J. (1999). Gender differences in treatment-seeking cocaine abusers — Implications for treatment and prognosis. The American Journal on Addictions, 8, 300 – 311. McCaughrin, W. C., & Howard, D. L. (1996). Variation in access to outpatient substance abuse treatment: Organizational factors and conceptual issues. Journal of Substance Abuse, 8, 403 – 415. McLellan, A. T., Kushner, H., Metzger, D., Peters, R., Smith, I., Grissom, G., Pettinati, H., & Argeriou, M. (1992). The fifth edition of the Addiction Severity Index. Journal of Substance Abuse Treatment, 9, 199 – 213. Messina, N., Wish, E., & Nemes, S. (2000). Predictors of treatment outcomes in men and women admitted to a therapeutic community. American Journal of Drug and Alcohol Abuse, 26, 207 – 227. Miller, W. R., & Tonigan, J. S. (1996). Assessing drinkers’ motivation for change: The Stages of Change Readiness and Treatment Eagerness Scale (SOCRATES). Psychology of Addictive Behaviors, 10, 81 – 89. Miller, W. R., Tonigan, J. S., & Longabaugh, R. (1995). The Drinker Inventory of Consequences (DrInC). Rockville, MD: NIAAA, US Department of Health and Human Services. Moras, K. (1998). Behavioral therapies for female drug users: An efficacyfocused review. In C. L. Wetherington, & A. B. Roman (Eds.), Drug addiction research and the health of women ( pp. 197 – 222). Rockville, MD: National Institute on Drug Abuse. Mulvaney, F. D., Brown, L. S., Alterman, A. I., Sage, R. E., Cnaan, A.,

28

L. Downey et al. / Journal of Substance Abuse Treatment 25 (2003) 19–28

Cacciola, J., & Rutherford, M. (1999). Methadone-maintenance outcomes for Hispanic and African-American men and women. Drug and Alcohol Dependence, 54, 11 – 18. Nelson-Zlupko, L., Kauffman, E., & Dore, M. M. (1995). Gender differences in drug addiction and treatment: Implications for social work intervention with substance-abusing women. Social Work, 40, 45 – 54. Ploeger-Dizon, C. A., Beretta, G. R., & Krupski, A. (1997). Tobacco, alcohol, and other drug abuse trends in Washington State: 1997 Report. Olympia, WA: Washington State Department of Social and Health Services, Division of Alcohol and Substance Abuse. Power, R., Hartnoll, R., & Chalmers, C. (1992). The role of significant life events in discriminating help-seeking among illicit drug users. International Journal of the Addictions, 27, 1019 – 1034. Project MATCH Research Group. (1997). Matching alcoholism treatments to client heterogeneity: Project MATCH posttreatment drinking outcomes. Journal of Studies on Alcohol, 58, 7 – 29. Robles, R. R., Marrero, C. A., Matos, T. D., Colo´n, H. M., Cancel, L. I., & Reyes, J. C. (1998). Social and behavioral consequences of chemical dependence. In C. L. Wetherington, & A. B. Roman (Eds.), Drug addiction research and the health of women ( pp. 355 – 364). Rockville, MD: National Institute on Drug Abuse. Sanchez-Craig, M., Spivak, K., & Davila, R. (1991). Superior outcome of females over males after brief treatment for the reduction of heavy drinking: Replication and report of therapist effects. British Journal of Addiction, 86, 867 – 876. Schliebner, C. T. (1994). Gender-sensitive therapy: An alternative for women in substance abuse treatment. Journal of Substance Abuse Treatment, 11, 511 – 515. Schneider, K. M., Kviz, F. J., Isola, M. L., & Filstead, W. J. (1995). Evaluating multiple outcomes and gender differences in alcoholism treatment. Addictive Behaviors, 20, 1 – 21. Sobell, L. C., Kwan, E., & Sobell, M. B. (1995). Reliability of a drug history questionnaire (DHQ). Addictive Behaviors, 20, 233 – 241. Stark, M. J., Campbell, B. K., & Brinkerhoff, C. V. (1990). ‘‘Hello, may we help you?’’ A study of attrition prevention at the time of the first phone contact with substance-abusing clients. American Journal of Drug and Alcohol Abuse, 16, 67 – 76.

Substance Abuse and Mental Health Services Administration. Office of Applied Studies. (1997). National Household Survey on Drug Abuse: Population Estimates 1996. Rockville, Md: National Institute on Drug Abuse, Division of Epidemiology and Statistical Analysis. Substance Abuse and Mental Health Services Administration. Office of Applied Studies. (1998). National Household Survey on Drug Abuse: Main Findings 1996. Rockville, Md: National Institute on Drug Abuse, Division of Epidemiology and Statistical Analysis. Substance Abuse and Mental Health Services Administration. Office of Applied Studies. (1999). Uniform Facility Data Set (UFDS): 1997 — Data on Substance Abuse Treatment Facilities. Rockville, MD: SAMHSA. Thom, B. (1986). Sex differences in help-seeking for alcohol problems: 1. The barriers to help-seeking. British Journal of Addiction, 81, 777 – 788. Thom, B. (1987). Sex differences in help-seeking for alcohol problems: 2. Entry into treatment. British Journal of Addiction, 82, 989 – 997. Toneatto, A., Sobell, L. C., & Sobell, M. B. (1992). Gender issues in the treatment of abusers of alcohol, nicotine, and other drugs. Journal of Substance Abuse, 4, 209 – 218. Veach, L. J., Remley, T. P., Kippers, S. M., & Sorg, J. D. (2000). Retention predictors related to intensive outpatient programs for substance use disorders. American Journal of Drug and Alcohol Abuse, 26, 417 – 428. Wallen, J. (1992). A comparison of male and female clients in substance abuse treatment. Journal of Substance Abuse Treatment, 9, 243 – 248. Wechsberg, W. M., Craddock, S. G., & Hubbard, R. L. (1998). How are women who enter substance abuse treatment different than men?: A gender comparison from the Drug Abuse Treatment Outcome Study (DATOS). Drugs & Society, 13, 97 – 115. Weisner, C., & Schmidt, L. (1992). Gender disparities in treatment for alcohol problems. JAMA, 268, 1872 – 1876. Weiss, R. D., Martinez-Raga, J., Griffin, M. L., Greenfield, S. F., & Hufford, C. (1997). Gender differences in cocaine dependent patients: A 6 month follow-up study. Drug and Alcohol Dependence, 44, 35 – 40. Williams, L. D., Mason, B. J., Goldberg, G., & Cutler, R. B. (1996). Women and minorities representation in alcoholism treatment research. Psychopharmacology Bulletin, 32, 201 – 204.