Treatment outcomes for methamphetamine users: California Proposition 36 and comparison clients.
Keywords--criminal offenders, methamphetamine, treatment outcomes, treatment performance
Methamphetamine (meth) abuse/dependence has been an increasing part of the overall drug problem in the United States over the past two decades, particularly impacting California. The offender population eligible for substance abuse treatment under Proposition 36 (Prop. 36) in California has contributed disproportionately to meth treatment admissions: In 2005-06, 57% of Prop. 36 treatment admissions reported meth or other amphetamines as their primary drug, while for all California treatment admissions, meth constituted 37% of treatment admissions in 2005 and 36% in 2006 (SAMHSA 2011; Urada et al. 2008). The magnitude of the meth problem in the California Prop. 36 population has motivated the following study focusing on characteristics, treatment performance, and treatment outcomes of this population. In addition, we examine whether performance and outcome measures differ from those of other subgroups of state substance abuse treatment system clients by comparing Prop. 36 meth users to Prop. 36 clients whose primary drug was not meth and to non-Prop. 36 clients (meth users and users of other substances). Results provide a statewide perspective on treatment performance and outcomes for meth abusers, and in particular for the Prop. 36 offender population, during a period in which there was considerable focus on the implementation of evidence-based approaches for treatment among these populations.
CALIFORNIA'S PROPOSITION 36
Proposition 36 (the California Substance Abuse and Crime Prevention Act of 2000) was enacted in 2001. It allows nonviolent drug offenders to receive treatment as an alternative to incarceration or probation/parole without treatment. Prop. 36 was designed to improve public safety by decreasing drug-related crime, reduce costs by reserving jail and prison space for serious and violent offenders, and improve public health by reducing drug abuse using effective treatment strategies (Drug Policy Alliance 2006). For the first-year Prop. 36 cohort (2001/02), half of whom were meth users, analyses found a benefit-cost ratio of two to one over a 42-month follow-up period (Hawken et al. 2008).
TRENDS IN METH USE
Meth use/abuse has increased dramatically in the United States since the early 1990s and continues to be a major concern for law enforcement, drug treatment systems, and health and social service systems. Meth-use trends increased steeply during the first half of the 20002010 decade and decreased somewhat since reaching a peak mid-decade, but by 2010 again showed increases (SAMHSA 2011). Meth production increased fivefold from 1997 to 2003 as evidenced by the number of meth laboratory seizures by law enforcement (NDIC 2006). Even as recent declines in overall production and seizures were being noted in 2005-07, there were commensurate increases in foreign production and meth trafficking into the United States (Maxwell & Rutkowski 2008), and there have again been increases in lab seizures in 2009 and 2010 (DEA 2011). The National Survey on Drug Use and Health showed that the number of meth users doubled from 1998 to 2009 (SAMHSA 2010). Similar trends have been seen among arrestees in Arrestee Drug Abuse Monitoring (ADAM II) sites in meth-affected areas, with rates of meth-positive urine tests declining from 2003 levels, but leveling or increasing again by 2009 (ONDCP 2010).
As the prevalence of meth use has increased, the effect on the drug abuse treatment system has been marked. Nationwide, treatment admissions for meth/amphetamines increased more than sixfold from 1992 to 2005, with meth/amphetamines accounting for 9% of admissions in 2005. Meth admissions declined to 6% in 2008. These national figures, however, fail to represent the effect of meth use in many affected states, including California; nor do they reflect the most recent increases in most states (SAMHSA 2011). In 2002, meth admissions to the public treatment system in California were five times the number in 1992 (Brecht, Greenwell & Anglin 2005), with continuing increases through 2005, when meth accounted for 37% of all California substance abuse treatment admissions; decreases seen in 2006-2008 have now attenuated, with meth accounting for 28% of California admissions in 2010 (SAMHSA 2011). Over half (57%) of the offenders participating in California's Prop. 36 in FY 2005-06 reported meth as their primary drug problem (Urada et al. 2008).
CONSEQUENCES AND CORRELATES OF METH USE
The increase in meth prevalence has been of particular social concern, in part due to meth's physiological, emotional, and neurocognitive effects, and related social consequences and costs (e.g., Glasner-Edwards et al. 2008; Westover, Nakonezny & Haley 2008; Vik 2007; McKetin et al. 2006; Smith et al. 2006). High rates of sexual risk behavior by meth users and injection behaviors by some meth users increase the risk for a number of health issues including HIV, hepatitis, and tuberculosis transmission (Brecht et al. 2009; Halkitis, Mukherjee & Palamar 2009; Gonzales et al. 2008; Baicy & London 2007; Scott et al. 2007; Shoptaw & Reback 2007; Volkow et al. 2007; London et al. 2004; Semple, Patterson & Grant 2004).
High rates of arrest, incarceration, and reported criminal behavior are found among meth users in the criminal justice system or in treatment (e.g., Urada et al. 2008; Urada, Longshore & Conner 2007; Brecht et al. 2006). Associations of meth use with impulsivity, hostility, and violence may play a role in the meth-criminality link (Lapworth et al. 2009; Ernst et al. 2008). Cartier, Farabee, and Prendergast (2006) found an association of past 30-day meth use and self-reported violent crime among drug-abusing offenders, and Darke and colleagues (2010) found that meth users were more likely to have committed violent crimes than were users of other drugs. However, no meth-violence link was found by Gizzi and Gerkin (2010), although their results showed an association between meth use (vs. use of other drugs) and property crime.
TREATMENT EFFECTS FOR METH USERS
Accumulating evidence indicates the effectiveness of substance abuse treatment in reducing meth use (e.g., Lee & Rawson 2008; Hillhouse et al. 2007; Brecht et al. 2006; Roll et al. 2006). Treatment effects occur both for clients referred through criminal justice channels and for those referred through other channels (Anglin et al. 2007; Brecht, Anglin & Dylan 2005). There is also evidence that meth users respond as well to treatment as most other clients (e.g., Rawson et al. 2008; Luchansky, Krupski & Stark 2007). Among Prop. 36 clients, meth users had treatment durations and treatment completion rates that were comparable to users of most other drugs and superior to those of heroin users (Urada et al. 2008; Urada, Longshore & Conner 2007). For a subsample of earlier Prop. 36 clients from selected sites, meth was also related to decreased drop-out rates, and there were improvements from intake to 12-month follow-up in measures from the Addiction Severity Index (Evans, Huang & Hser 2011; Evans, Li & Hser 2009). Improvements in psychosocial functioning measures were found by Joe and colleagues (2010) and Rowan-Szal and colleagues (2009) for male and female meth-using offenders in prison-based drug treatment programs, with improvements for meth-specific programs generally greater than for standard outpatient programs. The current study extends previous work with data from the Prop. 36 population to include assessment of outcome measures in several functional domains, as well as expanding comparisons for performance measures.
Data analyses were done in two parts to address the primary goals of this study: (1) describe Prop. 36 treatment clients whose primary drug was meth, and in order to provide a context for interpretation, compare them to Prop. 36 users of other drugs, non-Prop. 36 meth users, and non-Prop. 36 non-meth users; and (2) evaluate treatment performance/outcomes for Prop. 36 meth users and comparison groups.
The first part of the analyses focused on unduplicated admissions to treatment (i.e., unique clients) for FY 200607. Data were from the April 2008 file of the California Outcomes Measurement System (CalOMS), which records demographic, treatment, and outcome data at admission and discharge for California clients in publicly funded treatment; additional details regarding CalOMS data and use can be found in Rawson et al. (2008). In general, if a client had multiple admissions, the earliest one in the period was selected; however, if a client had admissions both from Prop. 36 and another referral source, the earliest Prop. 36 admission was selected and used for these analyses. Unduplicated admissions were divided into the following four groups by referral source and whether they used meth: Prop 36 clients reporting meth as their primary substance (Prop. 36 meth); all other Prop. 36 clients (Prop. 36 non-meth); non-Prop. 36 clients with meth as their primary substance (non-Prop. 36 meth); and all other non-Prop. 36 clients (non-Prop. 36 nonmeth). A general linear model was used to compare the four groups. Because the large sample sizes produced significant (p < .0001) differences for all characteristics even with small percentage differences, only selected characteristics and similarities/differences across groups are described.
The second part of the analyses assessed selected treatment performance and outcome measures for Prop. 36 meth clients and the comparison client groups based on treatment "episodes" for unduplicated admissions in FY 2006-07. Episodes of continuing care are defined as a contiguous sequence of one or more "service sets," with each service set delineated by an admission and discharge to a specific type of service/modality. A treatment episode can include a single type of treatment service/modality or a sequence of treatment types/modalities for a given client. Specifically, if a discharged client was subsequently readmitted within 30 days of the discharge, this readmission was not considered the beginning of a new episode, but part of a continuing episode of care. CalOMS discharge records through June 2008 were examined to allow for discharges that may have occurred long after the FY 2006-07 admission used in the first part of the analyses. From the FY 2006-07 unduplicated admissions, 145,947 episodes were identified and used in a description of CalOMS performance indicators (discharge status and duration of treatment). For analysis of treatment duration by type of treatment, episodes were labeled by the type of treatment occurring first in the service sequence. Earlier evaluation of CalOMS data found that about 85% of episodes included only one type of treatment. Assessment of the permutations of possible types of treatment within episodes was beyond the scope of the current analysis; however, for more details regarding the CalOMS evaluation, see Rawson et al. (2008).
From the set of episodes, a total of 73,805 had treatment outcome data available at discharge and could be included in the assessment of CalOMS outcomes. In the CalOMS system, detailed discharge data cannot be collected if a client is not available to provide those data (e.g., when a client leaves the treatment program earlier than planned; see Rawson et al. 2008 for more information).
CalOMS performance measures included discharge status (at the last available discharge record of an episode; completed vs. did not complete) and retention in treatment (based on number of days from the first admission to the last available discharge of the treatment episode). Retention was recoded as a dichotomous variable: less than 90 days vs. 90 days or more (a commonly used threshold for beneficial treatment; e.g., Hubbard et al. 1997) and less than 60 days vs. 60 days or more (a threshold of potential interest because of limitations on treatment duration in some areas due to fiscal constraints). Completion and retention were examined across all episodes for each of the groups. In addition, retention was examined for the two largest subsets of episodes depending on what type of treatment (or modality) initiated the episode: those consisting of outpatient treatment entirely or beginning with outpatient treatment (59% of episodes) and those consisting of or beginning with residential treatment designed to be 30 days or longer (18% of episodes). Groups were compared using a generalized linear model for binomial distribution, controlling for gender, race/ethnicity, age (younger than 35 vs. 35 or older), injection use, and years of primary drug use. Note that the age threshold of 35 years used for dichotomizing the age covariate is a category divider commonly used by the Community Epidemiological Work Group (2011) for reporting client characteristics and was close to the average age of the current sample at the beginning of treatment episode (average = 34.3 years).
CalOMS treatment outcome indicators were measured at admission to and last discharge from the treatment episode. Selected outcome indicators were included for five of the NOM (National Outcome Measures) domains (e.g., SAMHSA 2006) as operationalized in CalOMS (CA ADP 2011) and dichotomized for this analysis: substance use (no use vs. any days of use in past 30 days), employment (no days vs. any days of employment in past 30 days), criminal justice system-related activity (no arrests and no jail or prison days vs. any arrests, jail, or prison days in past 30 days); stability in housing (not homeless vs. homeless); and social connectedness (two measures, no days with serious family conflict vs. any days with serious family conflict in past 30 days, and no days with social support/recovery activities vs. any days of support/recovery activities in past 30). Groups were compared on their change from admission to discharge using a generalized estimating equation (GEE) approach for a binomial distribution, controlling for gender, race/ethnicity, age, injection use, and years of primary drug use.
The sample of unduplicated FY 2006-07 CalOMS admissions were distributed across the four comparison groups as follows: 14.3% Prop. 36 meth users; 20.6% non-Prop. 36 meth users; 11.1% Prop. 36 non-meth users (that is, primary users of substances other than meth); and 54.0% non-Prop. 36 non-meth users. Thus, meth users totaled 34.9% of the sample, and Prop. 36 users totaled 25.4% of the sample. Considering only Prop. 36 clients, more than half (56.3%) reported meth as their primary drug; however, only approximately one-fourth (27.6%) of the non-Prop. 36 clients were primary meth users (see Table 1).
The Prop. 36 meth users most closely resembled nonProp. 36 meth users on some characteristics and Prop. 36 non-meth users on other characteristics. On age, ethnicity, length of primary drug use history, and past-year injection use, the meth vs. non-meth distinction took precedence. In general, meth users were younger and more likely to be White or Hispanic, with a shorter primary drug history and lower rates of injection drug use than non-meth users. On gender, education, and employment, the Prop. 36 vs. non-Prop. 36 distinction was dominant, with Prop. 36 meth users more similar to Prop. 36 non-meth users than to non-Prop. 36 clients. In general, Prop. 36 client groups had lower percentages of women, a higher educational level, and higher rates of employment than did non-Prop. 36 groups. And, on modality, the Prop. 36 vs. non-Prop. 36 distinction strongly outstripped any meth vs. non-meth distinction, with Prop. 36 clients (both meth and non-meth) more likely to be admitted to outpatient treatment than were non-Prop. 36 groups.
Results for treatment performance indicators appear in Table 2.
Differences across the four groups were significant at p < .001 for all indicators. Prop. 36 meth users had a completion rate for episodes of treatment (37.2%) that was slightly lower than for other Prop. 36 clients (38.5%; p < .001) but higher than non-Prop. 36 clients (34.5% for meth users; p < .001) and statistically similar to clients with other primary drugs, controlling for other characteristics (35.9%; p = .69).
Prop. 36 meth and non-meth users had similar 90-day treatment retention rates, at 50.3% and 48.9%, respectively (p = .30). This Prop. 36 meth group retention rate was significantly higher than the retention rates for non-Prop. 36 groups (40.6% for non-Prop. 36 meth users and 36.5% for non-meth users; p < .001). Because expected retention can differ by type of treatment, we also examined retention specifically for those in outpatient treatment at episode admission and in longer-term residential treatment, the two most frequent treatment types; the patterns of results were similar to all modalities combined. Using a 60-day retention threshold, patterns of group similarities and differences mirrored those of 90-day retention but with higher rates across the board.
The overall picture of outcomes was that of a statistically significant improvement from admission to discharge for all domains across all groups (p < .001 for main effect of change across time). In addition, while groups differed significantly in their patterns of change (p < .001 for time-by-group interaction for all domains), differences were for the most part very small. Selected specific results are presented below in descriptive form. Prop. 36 meth users, similar to other groups, demonstrated substantial improvement in all outcome domains (see Table 3).
The percentage with any primary substance use in the past 30 days at discharge had declined to less than half the percentage at admission (from 50.9% to 24.8%). These relative declines were quite similar to those of Prop. 36 non-meth users (declines from 54.2% to 26.7%). Non-Prop. 36 meth users had only very slightly lower proportional declines in primary substance use (from 55.0% to 27.9%). The non-Prop. 36 non-meth group demonstrated slightly smaller proportional decreases (from 73.0% to 45.5%).
Prop. 36 meth users experienced decreases in self-reported criminal justice system (CJS) involvement (any arrests, jail, or prison days in past 30 days) similar to other Prop. 36 clients, with proportional decreases for both Prop. 36 groups being slightly greater than for non-Prop. 36 meth users and substantially greater than for non-Prop. 36 non-meth users. The CJS involvement rate decreased from 32.3% to 7.7% for Prop. 36 meth users and from 31.2% to 7.9% for other Prop. 36 clients.
Employment rates increased for Prop. 36 meth users (from 35.0% to 46.0%) similar to proportional increases for other Prop. 36 clients (from 32.5% to 42.1%). This level of proportional increase for the Prop. 36 groups was smaller than that for the non-Prop. 36 meth group, which had lower rates of employment overall but a larger proportional increase (from 18% to 28%), but it was larger than for the non-Prop. 36 non-meth group (from 21.0% to 25.1%).
Stability in housing increased for all groups from episode admission to discharge, produced primarily by decreases in homelessness for all groups and slight decreases in dependent living for Prop. 36 groups. The largest proportional decreases in homelessness were for the meth user groups (from 10.7% to 8.3% for Prop. 36 meth users and from 24.6% to 19.7% for non-Prop. 36 meth users).
Improvement was seen for Prop. 36 meth users as well as all other groups in the indicator of social connectedness. Decreases in family conflict were similar for the two Prop. 36 groups (from 8.2% to 5.4% for meth users and from 7.8% to 5.5% for non-meth users). But these Prop. 36 groups started at lower rates and had smaller decreases than did the non-Prop. 36 groups, which declined from 14.2% to 6.5% (meth) and 14.1% to 7.5% (non-meth).
Rates of participation in social support recovery activities also improved for all groups. The Prop. 36 meth group increased any participation from 49.0% to 69.8%, most similar to Prop. 36 non-meth users (increased from 44.2% to 65.2%). But these increases for the Prop. 36 groups were proportionally smaller than for the non-prop. 36 groups (from 43.6% to 74.6% for non-Prop. 36 meth users and from 29.7% to 57.1% for non-meth users).
The results of this study are consistent with a growing body of evidence showing that treatment outcomes for meth users are not generally inferior to those for non-meth users across a range of performance and outcome measures. This study has shown that Prop. 36 meth users experienced rates of treatment completion and retention very similar to those of Prop. 36 non-meth users and higher than for non-prop. 36 groups. These results are also consistent with previous assessments of earlier cohorts of Prop. 36 clients (e.g., Urada et al. 2008; Anglin et al. 2007).
Treatment outcomes as assessed through CalOMS treatment data showed improvement for Prop. 36 meth users in all outcome domains, with patterns of change from admission to discharge similar to those of Prop. 36 nonmeth users. For substance use and self-reported criminal justice involvement, Prop. 36 meth users showed a greater degree of improvement (in proportional terms) than did non-Prop. 36 meth and non-meth users. Results are again consistent with studies of earlier subsamples of Prop. 36 clients, e.g., with Evans, Huang, and Hser (2011) who found that recidivism was lower for meth-using Prop. 36 clients.
These results should further dispel lingering notions that meth abuse cannot be treated or that treatment may not work for meth users within the Prop. 36 offender population. Studies (cited in the introduction) suggest that meth users often exhibit mental health problems and/or cognitive deficits, which can complicate treatment. But there has been considerable effort in many counties to provide additional training related to meth dependence to treatment providers. Obtaining and coordinating appropriate services across service delivery systems (e.g., bridging drug treatment, criminal justice, and mental health service systems) remains a challenge according to some stakeholders, and this challenge may also be a barrier to optimal outcomes for meth users, as well as for other drug users. On the other hand, a growing body of research has developed and documented the potential superiority of some treatment approaches for meth-user offenders for at least short-term outcomes (e.g., Joe et al. 2010; Rowan-Szai et al. 2009). With increased evidence-based practice implementation, one would hope that treatment outcomes for meth use could improve systemwide.
These results should be interpreted within the context of several possible limitations. (1) Definitions of indicators of performance and outcomes may not yet optimally reflect a continuing care perspective appropriate to a chronic illness model. For example, a standard definition of treatment "completion" may be elusive because clients' individual treatment/recovery plans can differ and completion requirements may vary. (2) CaIOMS outcome measures were available for only about half the episodes, since collection of outcome information is not required for episodes with administrative discharges. The CalOMS evaluation explored potential bias in more detail (Rawson et al. 2008). (3) It is important to remember that percentages at admission and discharge and changes in those percentages represent the group as a whole and do not take into consideration individuals' specific patterns. Further, studies could explore characteristics of specific patternof-change subgroups (e.g., meth use to no meth use, no meth use to meth use, etc.) in order to identify barriers and facilitators of treatment outcomes. (4) Comparisons involving Prop. 36 meth users should be expanded to include additional subgroups of treatment clients. For example, the non-Prop. 36 groups in the current analysis included other criminal justice system (CJS) referrals to treatment; these CJS clients could be compared separately in future studies.
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Mary-Lynn Brecht, Ph.D. (a) & Darren Urada, Ph.D. (b)
(a) Research Statistician, Integrated Substance Abuse Programs, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA.
(b) Assistant Research Psychologist, Integrated Substance Abuse Programs, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA.
Please address correspondence and reprint requests to Mary-Lynn Brecht, Ph.D., UCLA Integrated Substance Abuse Programs, 11075 Santa Monica Blvd., Suite 100, Los Angeles, CA 90025; phone: 310-267-5275, fax: 310-473-7885, email: firstname.lastname@example.org
TABLE 1 Characteristics of Prop. 36 Primary Methamphetamine Users and Comparison to Prop. 36 Users of Other Primary Substances, Non-Prop. 36 Meth and Other Primary Drug Users, CalOMS FY 2006-07 (n = 172,400 Unduplicated Admissions) Methamphetamine Prop. 36 Non-Prop. 36 n = 24,686 n = 35,483 Age Group (a, b), % 25 & Younger 19.6 30.2 26-35 32.5 35.0 36-45 32.1 25.3 46+ 15.7 9.5 Race/Ethnicity, %n White 50.0 48.7 Hispanic 38.3 38.6 Black 3.4 4.0 Other 8.3 8.7 Women, % 28.9 49.7 Education, % Less than High School 38.3 43.7 High School 46.4 40.8 Some College/College Grad 15.3 15.5 Employment Status, % Full-Time 23.6 12.4 Part-Time 11.6 7.1 Unemployed 35.1 26.7 Not in Labor Force 29.7 53.8 Yrs Since First Use of Primary Drug, 13.4 (8.7) 12.2 (8.5) Mean (SD) Injection Drug Use in Past Year (a), % 12.2 15.0 Prior Alcohol or Drug Treatment (a), % 53.7 50.7 Modality, % Outpatient 84.7 63.3 Residential < 30 days 0.9 1.8 Residential [greater than or 12.6 27.1 equal to] 30 days Detox 1.8 7.8 NTP Detox 0.0 0.0 NTP Maintenance 0.0 0.1 Other Primary Drug Prop. 36 Non-Prop. 36 n = 19,186 n = 93,045 Age Group (a, b), % 25 & Younger 21.3 32.5 26-35 23.6 17.7 36-45 28.6 22.9 46+ 26.6 26.9 Race/Ethnicity, %n White 32.0 40.2 Hispanic 33.5 32.8 Black 27.4 20.1 Other 7.2 6.9 Women, % 23.3 34.3 Education, % Less than High School 39.7 45.3 High School 42.7 35.7 Some College/College Grad 17.6 19.0 Employment Status, % Full-Time 20.3 13.0 Part-Time 11.1 7.6 Unemployed 30.5 20.7 Not in Labor Force 38.1 58.7 Yrs Since First Use of Primary Drug, 17.6 (10.9) 16.9 (12.9) Mean (SD) Injection Drug Use in Past Year (a), % 17.2 22.8 Prior Alcohol or Drug Treatment (a), % 52.1 48.7 Modality, % Outpatient 81.7 54.3 Residential < 30 days 0.9 1.5 Residential [greater than or 12.9 14.5 equal to] 30 days Detox 2.2 12.4 NTP Detox 0.6 6.1 NTP Maintenance 1.9 11.4 (a) the number of cases with missing data for any of these characteristics ranged from 68 to 958. (b) Note that Prop. 36 is for clients 18 years and older. Clients younger than 18 are included in the "25 & younger" category for non-Prop. 36 clients. TABLE 2 CalOMS Performance Indicators for Prop. 36 Primary Methamphetamine Users and Comparison to Prop. 36 Users of Other Primary Substances, Non-Prop. 36 Meth and Other Primary Drug Users' (n = 145,947 Episodes) Methamphetamine Prop. 36 Non-Prop. 36 n = 21,449 n = 31,512 Completed Treatment, % 37.2 34.5 Retention [greater than or equal to] 50.3 40.6 90 Days, % (Across All Types of Treatment) Retention [greater than or equal to] 61.0 51.3 60 Days, % (Across All Types of Treatment) For Outpatient Modality (b) % with Retention [greater than or 51.7 46.6 equal to] 90 Days % with Retention [greater than or 61.9 57.9 equal to] 60 Days For Residential [greater than or equal to] 30 Days Modality (c) % with Retention [greater than or 48.7 41.6 equal to] 90 days % with Retention [greater than or 63.5 54.4 equal to] 60 days Other Primary Drug Prop. 36 Non-Prop. 36 n =16,423 n = 76,563 Completed Treatment, % 38.5 35.9 Retention [greater than or equal to] 48.9 36.5 90 Days, % (Across All Types of Treatment) Retention [greater than or equal to] 59.9 46.6 60 Days, % (Across All Types of Treatment) For Outpatient Modality (b) % with Retention [greater than or -50.7 48.9 equal to] 90 Days % with Retention [greater than or 61.8 61.6 equal to] 60 Days For Residential [greater than or equal to] 30 Days Modality (c) % with Retention [greater than or 47.1 36.5 equal to] 90 days % with Retention [greater than or 59.2 47.7 equal to] 60 days (a) Of the 145,947 episodes, a few had missing or invalid data for discharge status (n = 1,713) or retention (n = 1,494). (b) Outpatient treatment (as the only type of treatment in an episode or as the first of a sequence of admission-discharge service sets within an episode) was the most common for all groups: 83% of Prop. 36 meth group, 58% of non-Prop. 36 meth group, 80% of Prop. 36 non-meth, and 50% of non-Prop. 36 non-meth. (c) Residential treatment designed to be > 30 days (as the only type of treatment in an episode or as the first of a sequence of admission-discharge service sets within an episode) was the second most common for all groups: 13% of Prop. 36 meth group, 29% of non-Prop. 36 meth group, 14%n of Prop. 36 non-meth, and 16% of non-Prop. 36 non-meth. TABLE 3 CalOMS Outcomes for Prop. 36 Primary Methamphetamine Users and Comparison to Prop. 36 Users of Other Primary Substances, Non-Prop. 36 Meth and Other Primary Drug Users a (n = 73,805 Episodes) Methamphetamine Prop. 36 Non-Prop. 36 n = 11,345 n = 16,118 Primary Drug Use: Any Use in Past 30 Days, % Admission 50.9 55.0 Discharge 24.8 27.9 CJS Involvement (Any Arrests, Jail or Prison Days) In Past Admission 32.3 23.8 30 Days, % Discharge 7.7 6.2 Employed (Current), % Admission 35.0 18.0 Discharge 46.0 28.0 Employed or Enrolled in School/Training (Current), % Admission 38.3 25.9 Discharge 50.9 38.0 Living Situation (Primary Status in Past 30 Days), % Admission 10.7 24.6 Homeless Discharge 8.3 19.7 Family Conflict (Any Days in Past 30 Days), % Admission 8.2 14.2 Discharge 5.4 6.5 Any Days with Social Support Recovery Activities (in Past Admission 49.0 43.6 30 Days), % Discharge 69.8 74.6 Other Primary Drug Prop. 36 Non-Prop. 36 n = 8,184 n = 38,158 Primary Drug Use: Any Use in Past 30 Days, % Admission 54.2 73.0 Discharge 26.7 45.5 CJS Involvement (Any Arrests, Jail or Prison Days) In Past Admission 31.2 14.5 30 Days, % Discharge 7.9 5.3 Employed (Current), % Admission 32.5 21.0 Discharge 42.1 25.1 Employed or Enrolled in School/Training (Current), % Admission 39.3 36.7 Discharge 50.1 40.8 Living Situation (Primary Status in Past 30 Days), % Admission 14.6 24.3 Homeless Discharge 12.3 21.0 Family Conflict (Any Days in Past 30 Days), % Admission 7.8 14.1 Discharge 5.5 7.5 Any Days with Social Support Recovery Activities (in Past Admission 44.2 29.7 30 Days), % Discharge 65.2 57.1 (a) Results are reported for episodes which had valid data at both admission and discharge for specific outcome indicator. Number of episodes with missing data: criminal justice involvement (22), family conflict (20,852; measures not collected by all agencies), all other outcomes (0).
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|Author:||Brecht, Mary-Lynn; Urada, Darren|
|Publication:||Journal of Psychoactive Drugs|
|Date:||Sep 1, 2011|
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