Predictors of methadone program non-retention for opioid analgesic dependent patients

Journal of Substance Abuse Treatment 44 (2013) 52–60

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Journal of Substance Abuse Treatment

Predictors of methadone program non-retention for opioid analgesic dependent patients Joseph Cox, M.D., M.Sc. a, b, c, d,⁎, Robert Allard, M.D.C.M., M.Sc. c, d, Emilie Maurais, M.Sc. c, Noreen Haley a, Chris Small, B.B.A. a a b c d

Mental Health and Addiction Services, Cape Breton District Health Authority, Sydney, Nova Scotia, Canada B1P 1P3 School of Education, Health and Wellness, Cape Breton University, Sydney, Nova Scotia, Canada B1P 6L2 Public Health Department, Montreal Health and Social Services Agency, Montreal, Quebec, Canada H2L 1M3 Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Quebec, Canada H3A 1A2

a r t i c l e

i n f o

Article history: Received 2 August 2011 Received in revised form 7 February 2012 Accepted 19 March 2012 Keywords: Treatment retention Methadone maintenance program Opioid analgesics Health care utilization

a b s t r a c t This study evaluates loss to follow-up in a methadone maintenance treatment (MMT) program for patients dependent on opioid analgesics in a community in eastern Canada. Data were collected using the Addiction Severity Index Lite. The probability of loss to follow-up was evaluated using a time-to-event analysis. Involuntary and voluntary program discharges were treated separately as the outcomes of interest. Multivariate Cox proportional hazards models were used to explore the role of various patient-related attributes. The probabilities of involuntary and voluntary discharges at 1 year were 20% and 14%, respectively. In this exploratory analysis, determinants of loss to follow-up were characteristics related to drug use history (e.g., use of sedatives) and its consequences (e.g., number of lifetime arrests), and differed for each outcome. Some determinants of involuntary discharge were modified by sex. Understanding predictors of specific loss to follow-up outcomes may help MMT programs improve patient retention. Crown Copyright © 2013 Published by Elsevier Inc. All rights reserved.

1. Introduction An increase in the number of suspicious deaths related to the overdose of “hillbilly heroin”, also known as oxycodone, was observed in Cape Breton, Nova Scotia in the early 2000s (CTV.ca News Staff, 2004; Gillis, 2004). When crushed and snorted or injected, this medication, sold as Oxycontin®, induces a rapid opioid intoxication or “high” (Weekes, 2006). A regional task force reported that most of the oxycodone available for non-medical use originated from physicians' prescriptions and that diversion of this medication for non-medical purposes was widespread (Weekes, 2006). The problem of prescription opioid abuse was not unique to Cape Breton, rather the problem has been observed in other small urban and rural communities where there is a scarcity of jobs, low employment and an abundance of elderly and disabled patients requiring pain medication (Cicero, Surratt, Inciardi, & Munoz, 2007; Fischer, Rehm, Kim, & Kirst, 2005; Lewis, 2006). In addition, recent studies on illicit drug use have demonstrated a shift from heroin towards prescription opioid medications in several large cities across North America (Center for Substance Abuse Research, June 6, 2011; Fischer & Rehm, 2007; Fischer, Rehm, Patra, & Cruz, 2006; Wu, Woody, Yang, & Blazer, 2011). ⁎ Corresponding author. 1301 Sherbrooke Street East, Public Health Department, Montreal Health and Social Services Agency, Montreal, Quebec, Canada H2L 1M3. Tel.: + 1 514 528 2400x3630; fax: + 1 514 528 2452. E-mail address: [email protected] (J. Cox).

These medications are rarely prescribed but obtained through the drug market or from friends or relatives (Center for Disease Control and Prevention & National Center for Injury Prevention and Control, 2011). The recommended treatment for opioid dependence is substitution using either methadone or buprenorphine. There is no universal definition for a methadone maintenance treatment (MMT) program and these programs are delivered in different ways and in a variety of settings within Canada (Luce & Strike, 2010). Programs are often classified according to the threshold or tolerance for ongoing drug use and policies regarding the frequency of urine toxicology screening tests, contingency management (patients' responsibility for methadone carry doses), and criteria leading to involuntary discharge from the program (Eriksen, Postnikoff, Rhode, & Wurtz, 2001). Program retention is often used as a measure of MMT effectiveness. Duration of treatment is associated with a variety of positive outcomes, including less risk of relapse and associated high-risk behaviours (e.g., sharing of syringes), less criminal activity and improved social functioning such as employment (Del Rio, Mino, & Perneger, 1997; Strike et al., 2005). Determinants of retention in MMT programs have been investigated by numerous researchers and usually in situations of heroin abuse. Some of the personal attributes thought to affect retention include age, sex, race, relationship status, employment, income, criminal history, mental health status, co-use of other substances, number of attempts at treatment, and treatment readiness/motivation (Anderson & Warren, 2004; Babst, Chambers, & Warner, 1971; Del Rio et al., 1997; Greenfield et al., 2007; Lehmann, Lauzon, & Amsel, 1993; Simpson, Joe, &

0740-5472/$ – see front matter. Crown Copyright © 2013 Published by Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.jsat.2012.03.002

J. Cox et al. / Journal of Substance Abuse Treatment 44 (2013) 52–60

Rowan-Szal, 1997; Strike et al., 2005). Other factors supporting retention and abstinence from heroin include higher daily methadone dose as well as additional psychosocial supports (Amato et al., 2009; Faggiano, Vigna-Taglianti, Versino, & Lemma, 2008; Farre, Mas, Torrens, Moreno, & Cami, 2002). Some of the weaknesses of these studies include an inadequate appreciation for the timing of loss to follow-up and treatment retention over time (Del Rio et al., 1997; Magura, Nwakeze, & Demsky, 1998). For this reason, the use of survival analysis is recommended (Magura et al., 1998). The reasons why a patient leaves care may be quite different from those leading to involuntary discharge. While patient loss to followup from drug treatment has traditionally been investigated as a summative outcome, it may be useful to consider components of the outcome (involuntary and voluntary discharge). Other researchers have also conceptualized loss to follow-up in different ways (e.g., early exit before and after treatment initiation) (Stevens, Radcliffe, Sanders, & Hunt, 2008). Little is known about patient retention in MMT programs for patients who are dependent on opioid analgesics, that are not prescribed. In this paper, we report on patient loss to follow-up (non-retention) and its determinants for patients who are dependent on opioid analgesics and receiving care in the Cape Breton Methadone Maintenance Program (CBMMP). We hypothesize that patient-related determinants of loss to follow-up will differ depending on whether the patient was voluntarily or involuntarily discharged. Understanding patient loss to follow-up is an important part of reporting on program effectiveness and improving MMT retention. 2. Materials and methods 2.1. Study design, population and program description This is a retrospective study of an open cohort of 246 patients admitted to the CBMMP from May 1, 2004 to May 1, 2007. The CBMMP was created through Addiction Services of the Cape Breton District Health Authority (CBDHA), Nova Scotia Department of Health and began operating in May 2004. All study participants were first-time admissions and represented all the patients enrolled in the program during this period. They contributed study information until the occurrence of the outcome of interest or until May 1, 2007. The CBMMP is a medium-threshold clinic with program policies oriented between abstinence-based (high-threshold) and harm reduction (low-threshold) approaches (Eriksen et al., 2001). Patients were eligible for treatment if they were at least 18 years of age, had used opioid analgesics for at least 1 year and had already made at least one attempt at treatment (e.g., detoxification). Patients were self-referred, referred by local health care professionals and addictions counsellors or transferred from a private clinic that closed suddenly. This clinic operated quite differently from the CBMMP: most patients received methadone daily at local pharmacies, underwent non-random urine screening and did not have an Addiction Severity Index (ASI) evaluation. At CBMMP program entry, patients were evaluated to verify opioid dependence and willingness to comply with program policies and procedures (American Psychiatric Association, 2000). Each patient signed a treatment contract and underwent a medical evaluation; patients were provided a prescription for methadone (taken daily as a witnessed ingestion) if urine toxicology testing was positive for opioid metabolites. Patients were seen weekly for methadone titration so as to alleviate opioid withdrawal symptoms (dysphoric mood, nausea, insomnia, etc.) and craving (American Psychiatric Association, 2000). Patients provided supervised random urine samples two to four times each month at the beginning of the program and received a letter of warning each time urine tests were positive for opioids, cocaine or other substances/non-prescribed medications (e.g., amphetamines, benzodiazepines). Patients began receiving such letters after sufficient time had passed to reach a

53

methadone dose that minimized opioid withdrawal symptoms, usually within 6–8 weeks of admission. 2.2. Data collection Data for this analysis came from patient information collected during program-related clinical interviews. The ASI Lite was used; it is a standardized clinical tool for drug dependent patients and has been established as a reliable and valid instrument (McLellan, Luborsky, Woody, & O'Brien, 1980). These data were collected at program entry and every 6 months. For those patients who transferred in from a local private clinic, ASI-related information for the period before starting methadone at that clinic was obtained. Therefore, similar information for these patients was available and used. Data at baseline were primarily used for these analyses. All information linked with patient identifiers was stored on CBMMP computers with password protected access. Information was managed to comply with current confidentiality policies of the CBDHA and the Tri-Council Policy Statement: Ethical Conduct for Research Involving Humans (CBDHA, 2005; Interagency Secretariat on Research Ethics, 2005). The current analysis was approved by the Research Ethics Board at the CBDHA. 2.3. Variables Patient loss to follow-up was the outcome of interest. Patients experienced this outcome if they were involuntarily discharged from the program or if they voluntarily interrupted (voluntary discharge) methadone therapy for more than 7 days. Patients could be involuntarily discharged if they were aggressive towards clinic staff, or if they continued to use opioid analgesics or other non-prescribed substances (e.g., cocaine, amphetamines, and benzodiazepines) despite numerous attempts to stay within the program. Time in the program was determined based on the cumulative number of days from program admission to the date of discharge (involuntary and voluntary), date of transfer to another MMT program, or as of May 1, 2007. For patients who transferred in from a private clinic, time in the program represents the start of participation in the CBMMP and does not reflect involvement in prior therapy. Retained patients included those who were active in the program as of May 1, 2007, those who completed the program (patients who were stabilized on and voluntarily tapered from methadone) as well as those who had transferred. Program transfers were primarily for patients to begin working, usually in western Canada. Methadone dose was defined as the amount of methadone taken daily between 3 to 6 months after admission. For patients transferring into the program (usually after the 6–8 weeks methadone titration phase), the methadone dose at entry was used if not available on follow-up evaluation. While the modeling and analytic strategies were largely exploratory in nature, the selection of potential predictors of loss to follow-up was informed by the Gelberg–Anderson Behavioral Model for Vulnerable Populations and recent research by Villafranca and colleagues (Gelberg, Andersen, & Leake, 2000; Villafranca, McKellar, Trafton, & Humphreys, 2006). This model includes a variety of characteristics (predisposing, enabling and need) reflecting traditional and vulnerable domains (Fig. 1). The various characteristics examined are listed in Fig. 1. Information on serological testing (human immunodeficiency virus and hepatitis C virus [HCV]) was incomplete for the majority of patients and therefore could not be used in these analyses. Most variables were dichotomized at the median or transformed as categorical for ease of interpretation. 2.4. Statistical analysis All patients enrolled in the program were included in the analysis. Study data were derived from completed ASI forms and analysed using SAS version 9.1.3 (SAS Institute Inc., Cary, NC).

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J. Cox et al. / Journal of Substance Abuse Treatment 44 (2013) 52–60

OUTCOME

CHARACTERISTICS Predisposing variables

Enabling variables

TRADITIONAL DOMAIN Age Sex Education Relationship status Paid work

TRADITIONAL DOMAIN Personal income and debt Responsible for a dependent

VULNERABLE DOMAIN History of depression and/or anxiety History of sexual and/or physical abuse Substance use (e.g. opiates, sedatives, cocaine, etc.) Injection drug use History of drug detoxification efforts History of arrests

VULNERABLE DOMAIN Living with someone with a drug and/or alcohol problem Conflict with family and/or others Methadone dose Money spent on drugs

Need variables

Health services utilization

TRADITIONAL DOMAIN Chronic medical problem

VULNERABLE DOMAIN Perceived trouble associated with medical and drug problem(s) Perceived importance of treatment for medical and drug problem(s) Current depression and/or anxiety

Patient’s loss to follow-up

Fig. 1. Gelberg-Anderson Behavioral Model for Vulnerable Populations adapted for patients participating in a methadone maintenance program. Adapted from (Gelberg et al., 2000). Predisposing characteristics represented factors that occur before illness onset and include demographic traits such as age, sex and employment status. Enabling characteristics represented various “resources” and social support factors; these include income and debt. Need characteristics represented severity of illness factors including drug and medical problems. Traditional domains are those usually used in the Behavioral Model. Vulnerable domains represent health and behavioural factors important to the vulnerable population under study.

Patient loss to follow-up (involuntary and voluntary discharge) was analysed using the Kaplan–Meier method where participants who had completed the program or had transferred to another program were censored (ceased contributing to the person-days of follow-up). Because patient factors may influence voluntary and involuntary discharge differently, analyses were done separately for each of these outcomes. Therefore, the data sets for each of these analyses excluded subjects having the alternative outcome. As stated, the modeling and analytic strategies were exploratory. Multivariate Cox proportional hazards models were used to investigate the role of various patient-related attributes. Unadjusted and adjusted hazard ratios (aHR) with 95% confidence intervals (CIs) were calculated. Upon completing the univariate analysis, only those variables meeting a significance level of p b .25, having a 95% CI suggestive of an association and those with substantive importance (age and sex) were entered into the multivariate regression models. The method of variable selection for modeling was the one proposed by Hosmer and Lemeshow; the final model was derived by sequentially removing variables of least statistical significance and examining the change in parameter estimates in order to identify potential confounding (Hosmer & Lemeshow, 1989; Selvin, 1996). In this instance, a change greater than 20% was considered significant. The selection of terms for testing interactions was informed by existing literature (Grella, Hser, Joshi, & Anglin, 1999; Petry & Bickel, 2000; Strike et al., 2005). A significance level of p b .05 was used for inclusion of covariates and interaction terms in the final model. An association with the outcome was considered significant when the 95% CI excluded the null value. One drawback of the analytical methods described above is that it does not treat the different type of failures in a combined fashion. It is possible to consider two outcomes in addition to censoring by applying Cox regression to competing risks (Lunn & McNeil, 1995). This was done to validate our results. The variable selection for these models followed the same procedures as for the separate multivariate Cox proportional hazards models.

3. Results A profile of the 246 subjects is provided in Table 1. The variables are presented according to the various domains of the Gelberg– Andersen Behavioral Model for Vulnerable Populations (Gelberg et al., 2000). Average age was 31 years (median, 28 years) and 68% were male. Approximately 25% of patients had greater than five lifetime arrests. The median personal income was $900 for the past 30 days and the median daily dose of methadone was 90 mg. For the majority of patients admitted to the program (75%), oxycodone (Oxycontin®, Percocet®) was reported as the drug of choice, followed by hydromorphone (17%) and morphine (3%). Nearly half of patients reported chronic medical problems, primarily chronic HCV infection/ liver problems, back and musculoskeletal pain, asthma and chronic lung problems. Approximately 42% of patients entered the program as a transfer from a local private clinic that closed suddenly. 3.1. Patient retention Patient (N = 246) disposition after 3 years of the program (status as of May, 2007) was: 63% in treatment (n = 154, receiving methadone; n = 1, program completion), 10% (n = 24) transferred to other programs, 27% (n = 67) discharged from the program (involuntarily [17%, n = 42] and voluntarily [10%, n = 25]). Mean time in the program was 306 days (range, 21–1,071 days). At 1 year, the combined probability (every patient; N = 246) of involuntary and voluntary discharge was 28%; that is, 72% of patients were retained (in treatment as defined above or transferred) (Fig. 2A). The 1 year probability of involuntary discharge was 20% (excluding voluntarily discharged patients; total n = 221), and the probability of voluntary discharge at 1 year was 14% (excluding involuntarily discharged patients; total n = 204) (Fig. 2B and C). The sum of the probabilities for each outcome exceeds the combined probability because retained patients are common to both subgroups.

J. Cox et al. / Journal of Substance Abuse Treatment 44 (2013) 52–60 Table 1 Profile of participants (N = 246) according to predisposing, enabling and need characteristics; CBMMP, Canada, May 2004–May 2007. Variables

N = 246 (%)a

Predisposing Sex Female 80 (32.5) Mean age (median) 31(28) Education (≤ 11 years) 110 (44.7) Relationship status Single 151(61.6) Married, common-law 60 (24.5) Other (divorced, separated, widow) 34 (13.9) Days of paid workb 0 174 (70.7) 1–7 13 (5.3) ≥ 8 59 (24.0) Number of lifetime arrests 0 87 (35.5) 1–5 98 (40.0) ≥ 6 60 (24.5) Awaiting charges, trial, or sentence 47 (19.2) Lifetime history of serious depression 192 (78.0) Lifetime history of serious anxiety 199 (80.9) Lifetime history of sexual abuse 68 (27.8) Lifetime history of physical abuse 121 (49.2) Lifetime history of opioid use (N 5 years) 104 (42.4) Use of other substancesb Alcoholc 36 (14.7) Barbiturates 20 (8.2) Sedatives 100 (40.7) Cocaine 84 (34.1) Amphetamines 26 (10.6) Cannabis 121 (49.2) Lifetime history of injection drug use 150 (61.2) Route of opioid administrationb IV 115 (46.7) Other 131 (53.3) Lifetime history of drug detoxification admissions 224 (91.4) Enabling b Approximate personal income (all sources; N 900.00 Cdn$) 120 (49.0) Any personal debt 106 (43.3) Responsible for at least 1 dependent 92 (37.6) Living with someone with a drug problem 35 (14.3) Living with someone with an alcohol problem 25 (10.2) b Days of serious conflict with family (≥ 1 day) 74 (30.2) b Days of serious conflict with others (≥ 1 day) 44 (17.9) Methadone dose (median daily dose, mg)d 90 Money on drugsb (≤ 350.00 Cdn$) 123 (50.0) Need Chronic medical problems 119 (48.6) Depressionb 91 (37.1) b Anxiety 127 (51.6) Perceived trouble/bother associated with current medical problemb No real to slight problem 196 (80.0) Moderate to extreme problem 49 (20.0) Perceived trouble/bother associated with current drug problemb No real to slight problem 90 (36.9) Moderate to extreme problem 154 (63.1) b Perceived importance of treatment of drug problem now No to slight importance 84 (34.4) Moderate to extreme importance 160 (65.6) (continued on next page)

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Table 1 (continued) Variables Other covariates Transfere into the program Outcome of interest Mean time (days) in the program as of May 2007 (median) Range Status in the program as of May 2007 Active/Retained Transferred to another program Involuntary discharge Voluntary discharge

N = 246 (%)a 102 (41.5) 306 (190) 21–1071 154 (62.6) 25 (10.2) 42 (17.1) 25 (10.2)

Abbreviations: mg = milligram, Cdn = Canadian. a Percentage among respondents (sample size between 226 and 246). b Refers to past 30 days. c At least 1 day of use to intoxication. d Defined as the total daily milligram dose recorded at the first follow-up evaluation (usually within 3 months of admission) or the dose recorded for patients transferring into the program. e Transfers were from a neighbouring private clinic-based program which ended abruptly.

personal debt. In addition, interactions between several covariates were observed; associations between involuntary discharge and current use of sedatives (≥ 1 day/past 30 days) and a lifetime history of opioid use (N 5 years) were modified by sex (female). Specifically, use of sedatives makes males more likely to be involuntarily discharged compared with males who do not use sedatives while we were not able to show an effect of use of sedatives on involuntary discharge for females. With respect to a longer lifetime history of opioids, females with more than 5 years of opioid analgesics use were more likely to be involuntarily discharged compared with females with 5 years or less of opioid use. We were not able to show an effect of history of opioids for males. With respect to voluntary program discharges, the factors significantly associated were: sex (female), use of sedatives (≥ 1 day/past 30 days) and injection of opioids. No interactions between model covariates were found. No differences were observed between parameter estimates resulting from the competing risk models (data not shown) and those of the multivariate Cox proportional hazards models. 4. Discussion 4.1. Patient retention To the best of our knowledge, this is the first study to examine the retention of opioid analgesic-dependent patients in an MMT program located in a non-urban setting. Since 2004, Cape Breton's first methadone maintenance program has been successful in achieving a level of 1-year patient retention (72%) that is similar or higher than other programs in British Columbia (52%), the National Treatment Outcome Research Study in Britain (62%) and the Drug Abuse Treatment Outcome Study in the United States (15%–76%) (Anderson & Warren, 2004; Gossop, Marsden, Stewart, & Treacy, 2001; Simpson et al., 1997).

3.2. Determinants of loss to follow-up 4.2. Determinants of involuntary discharge Univariate results for loss to follow-up (involuntary or voluntary discharge) are presented in Table 2; a variety of predisposing, enabling and need factors were associated. While some of the factors were shared, others were specific to one outcome only. Tables 3 and 4 present the results of the multivariate Cox proportional hazards analysis of loss to follow-up outcomes. Independent factors significantly associated with involuntary discharge included greater than six lifetime arrests, serious conflict with others (≥ 1 day/past 30 days; non-family member), and reporting no

We identified several determinants of involuntary discharge, including a history with the justice system and the recent experience of interpersonal conflict. Together, these experiences represent the drug abuse-related harms and behaviours associated with and resulting from chronic drug dependence (Lu et al., 2009; Smythe & Caverson, 2008; United Nations Office on Drugs and Crime, n.d.). Involuntary discharges from MMT programs reflect to some extent the policies of the program. While it is usual practice of the CBMMP to

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J. Cox et al. / Journal of Substance Abuse Treatment 44 (2013) 52–60

1,0

A

Survival Function

Every patient (n = 246)

Censored Probability of loss to follow-up at one year

0,8

0,6

0,4

28% 0,2

0,0

1,0

Survival Function

B Excluding voluntarily discharged patients (n = 221)

% Loss to Follow-up

Censored Probability of loss to follow-up at one year

0,8

0,6

0,4

20% 0,2

0,0 1,0

C Excluding involuntarily discharged patients (n = 204)

Survival Function Censored Probability of loss to follow-up at one year

0,8

0,6

0,4

0,2

14%

0,0 0

90

180

270

360

450

540

630

720

810

900

990

1080

1170

1260

Time in Program as of May 2007 Fig. 2. Cumulative risks of loss to follow-up among patients participating in the CBMMP, Canada, May 2004-May 2007. (A) Cumulative risk of any loss (every patient; N = 246), (B) Cumulative risk of loss due to involuntary discharge (excludes voluntarily discharged patients; N = 221), (C) Cumulative risk of loss due to voluntary discharge (excludes involuntarily discharged patients; N = 204).

retain patients based on any observed improvement (e.g., less frequent drug use, obtaining a drug counsellor), there is zero tolerance for any violations of code of conduct. Therefore, any occurrence of aggressive or illegal behaviours results in immediate discharge. Indeed, code of conduct infractions accounted for most of the observed involuntarily discharges and not ongoing drug use. Another possible explanation for the occurrence of involuntary discharges, because of code of conduct issues, is the important link

between Axis II diagnoses and less program retention (Peles, Schreiber, Sason, & Adelson, 2011). The cornerstone symptoms of difficulty controlling destructive behaviours and a disregard for others, synonymous with borderline and antisocial personality disorders, respectively, could explain some of our findings (American Psychiatric Association, 2000). The association between involuntary discharge and the experience of serious conflict with non-family members also supports this assertion. Unfortunately, there was no

J. Cox et al. / Journal of Substance Abuse Treatment 44 (2013) 52–60 Table 2 Cox proportional hazards univariate regression of patient characteristics (predisposing, enabling and need) relative to the outcomes involuntary discharge (n = 42) and voluntary discharge (n = 25). Variables Predisposing Sex Age (years) Education (≤ 11 years) Relationship status (married, common law vs. other) Days of paid work (≥ 8 days) Number of lifetime arrests (≥ 6 arrests) Awaiting charges, trial, or sentence Lifetime history of serious depression Lifetime history of serious anxiety Lifetime history of sexual abuse Lifetime history of physical abuse Lifetime history of opioid use (N 5 years) Use of other substancesa Alcoholb Barbituates Sedatives Cocaine Amphetamines Cannabis Lifetime history of injection drug use Injecting opioids (vs. other routes)a Lifetime history of drug detoxification admissions Enabling Approximate personal incomea (N $900.00/30 days) Any personal debt Responsible for at least 1 dependent Living with someone with a drug problem Living with someone with an alcohol problem Days of serious conflict with familya (≥ 1 day) Days of serious conflict with othersa (≥ 1 day) Methadone dosec Money on drugsa (≤ $350) Need Chronic medical problems Depressiona Anxietya Perceived trouble/bother associated with current medical problema (moderate to extreme problem) Perceived trouble/bother associated with current drug problema (Moderate to Extreme Problem) Perceived importance of treatment of drug problem nowa (moderate to extreme importance) Other confounders/covariates Transferd into the program

Involuntary discharge Voluntary discharge HR (95% CI) HR (95% CI) 0.55 1.02 0.90 0.56

(0.26, (0.99, (0.49, (0.27,

1.16) 1.06) 1.66) 1.16)

2.72 1.04 0.75 0.41

(1.22, (0.99, (0.34, (0.14,

6.05)⁎ 1.08) 1.65) 1.21)

1.09 (0.50, 2.37) 3.34 (1.80, 6.19)⁎

1.30 (0.48, 3.49) 2.73 (1.22, 6.09)⁎

1.93 (1.01, 3.68)⁎ 1.05 (0.50, 2.22)

2.77 (1.22, 6.30)⁎ 2.07 (0.61, 6.97)

1.78 0.87 1.53 2.12

(0.74, (0.43, (0.82, (1.13,

4.29) 1.77) 2.86) 3.99)⁎

6.74 2.85 1.86 0.62

(0.90, (1.30, (0.82, (0.26,

50.44) 6.26)⁎ 4.22) 1.49)

1.74 0.96 3.66 1.62 1.36 0.94 2.26

(0.85, (0.38, (1.90, (0.88, (0.57, (0.51, (1.08,

3.56) 2.46) 7.05)⁎ 2.97) 3.25) 1.73) 4.73)⁎

0.78 0.38 3.35 2.14 1.08 1.17 2.40

(0.23, (0.05, (1.48, (0.97, (0.32, (0.53, (0.90,

2.62) 2.83) 7.58)⁎ 4.74) 3.64) 2.58) 6.41)

2.11 (1.08, 4.12)⁎ 1.07 (1.04, 1.11)⁎

3.43 (1.29, 9.16)⁎ 1.05 (0.99, 1.11)

0.55 (0.29, 1.04)

1.48 (0.66, 3.29)

0.55 (0.29, 1.06) 0.42 (0.21, 0.84)⁎

1.89 (0.83, 4.28) 0.76 (0.34, 1.71)

1.18 (0.54, 2.54)

1.06 (0.36, 3.10)

1.45 (0.61, 3.46)

2.23 (0.83, 5.99)

1.59 (0.85, 2.95)

2.26 (1.02, 4.99)⁎

2.94 (1.56, 5.56)⁎

3.50 (1.57, 7.84)

1.00 (0.98, 1.01) 1.13 (0.61, 2.12)

0.98 (0.96, 1.00) 1.76 (0.80, 3.88)

1.08 1.48 1.34 1.25

1.84 1.96 2.48 2.86

(0.58, (0.80, (0.72, (0.64,

1.99) 2.76) 2.50) 2.47)

(0.79, (0.88, (1.01, (1.29,

4.28) 4.36) 6.06)⁎ 6.33)⁎

57

data collection on the presence of Axis II diagnoses; therefore, we cannot explore personality disorders as a mechanism for the observed associations. Program initiatives that reinforce appropriate behaviours and respect for program rules and provide mental health support and referral may help patients remain in treatment. Several studies have looked at sex differences in treatment retention and the results are inconsistent (Greenfield et al., 2007). None, however, have looked at it in terms of voluntary and involuntary discharge from treatment. The observed sex differences for a longer history of opioid analgesic use could be explained by unmeasured factors that have been shown to be associated with women's failure to maintain treatment such as severity of substance dependence, hostility and personality disorders (Cacciola, Rutherford, Alterman, McKay, & Snider, 1996; Greenfield et al., 2007; Petry & Bickel, 2000; Vaillant, 2003). Regarding other predictors of involuntary discharge, all have been documented in existing research on the topic, including the use of substances other than opioids such as sedatives (Babst et al., 1971; Deck & Carlson, 2005; DeMaria, Sterling, & Weinstein, 2000; Stevens et al., 2008). 4.3. Determinants of voluntary discharge The patient characteristics associated with voluntary discharge included sex, sedative use and drug injection. Why females and participants with concurrent substance use are not retained has already been discussed for involuntary discharge. Additional unmeasured factors that may explain the higher probability of voluntary discharge for females are lacking support from a spouse or a partner and childcare responsibilities (Anglin, Hser, & Booth, 1987; Greenfield et al., 2007). With respect to drug injection, some researchers have found a protective effect on program retention, specifically, for the period between assessment and program entry (Stevens et al., 2008). In that study, almost one-quarter of patients left the program either between assessment and program entry (16.7%) or early after program entry, that is, within 30 days (7.8%). For comparison with our study, the latter outcome corresponds best and only older age was found to be associated in that study. Our finding that current injection drug use was a determinant of program withdrawal may be due, in part, to how our outcome was defined. We did not limit program withdrawal to the first 30 days of treatment, allowing for more follow-up time and opportunity to detect this association. The importance of the drug injection act, i.e., the ritual of injection or needle fixation, and perhaps the severity of drug dependence, should not be underestimated, even in the context of receiving opioid agonist therapy (Centre for Addictions Research of British Columbia, 2006; Lu et al., 2009; Pates, McBride, Ball, & Arnold, 2001). 4.4. Determinants of overall loss to follow-up

1.95 (0.89, 4.26)

2.19 (0.81, 5.91)

1.35 (0.62, 2.95)

4.32 (1.00, 18.56)⁎

0.81 (0.38, 1.72)

0.32 (0.09, 1.07)

Sample size for analysis was n = 221 and n = 204 for involuntary and voluntary discharge outcomes, respectively. Abbreviations: HR = hazard ratio, CI = confidence interval. a Refers to past 30 days. b At least 1 day of use to intoxication. c Dose (mg) of methadone based on dose upon entry to program if not available on first evaluation. d Transfers were from a neighbouring private clinic-based program which ended abruptly. ⁎ p = .05.

Other studies on the topic of loss to follow-up in MMT programs highlight the importance of various program- and patient-related factors in program retention. Similar to our study, Villafranca et al. (2006) used a behavioural model to explore patient-related determinants and overall retention; they found that higher methadone dosage and greater treatment satisfaction were the strongest predictors of 1-year retention. Methadone dose has been consistently documented as one of the more important determinants of retention. For example, in one study, the risk of loss to follow-up was halved if the daily dose of methadone was between 60 and 79 mg compared with lower doses (Caplehorn & Bell, 1991). Another study showed that patient retention at 1 year was associated with methadone doses greater than 59 mg per day (Villafranca et al., 2006). We found no association between methadone dose and loss to follow-up (involuntary and voluntary discharge). The higher doses of methadone used by patients in the CBMMP likely reduced the importance of this factor in program discharges. In fact, almost

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Table 3 Cox proportional hazards multivariate regression of patient characteristics (predisposing and enabling) relative to involuntary discharge; n = 221, CBMMP, Canada, May 2004– May 2007. Variables

Predisposing Number of lifetime arrests (≥ 6) Sex Use of sedativesa Lifetime history of opioid use (N 5 years) Interaction terms Use of sedatives (among males) Use of sedatives (among females) Lifetime history of opioid use (N 5 years) (among males) Lifetime history of opioid use (N 5 years) (among females) Enabling Any personal debt Days of serious conflict with others (excluding family)a (≥ 1 day)

Without interaction terms

With interaction terms

Beta (SE)

p-value

aHR (95% CI)

Beta (SE)

p-value

aHR (95% CI)

0.99 (0.33) − 1.18 (0.43) 1.27 (0.36) 0.72 (0.34) – – – – –

0.003 0.006 b 0.001 0.033 – – – – –

2.70 0.31 3.56 2.05 – – – – –

(1.41–5.17) (0.13–0.71) (1.77–7.19) (1.06–3.98)

1.35 (0.36) − 1.04 (0.99) – – – 1.67 (0.40) − 0.40 (0.73) 0.30 (0.37) 2.49 (0.89)

b 0.001 0.293 – – – b 0.001 0.590 0.427 0.004

3.84 (1.90–7.77) 0.35 (0.05–2.46) – – – 5.33 (2.44–11.66) 0.67 (0.16–2.84) 1.35 (0.65–2.79) 12.03 (2.19–66.07)

− 0.82 (0.37) 1.48 (0.38)

0.027 b 0.001

0.44 (0.21–0.91) 4.40 (2.11–9.21)

− 1.08 (0.40) 1.89 (0.41)

0.007 b 0.001

0.34 (0.16–0.74) 6.63 (2.96–14.88)

Abbreviations: SE = standard error, aHR = adjusted hazard ratio, CI = confidence interval. a Refers to past 30 days.

three-quarters of patients were taking methadone doses greater than 75 mg per day. We also found no association between the greater number of detoxification admissions and patient retention in our multivariate models. This is in contrast to research suggesting that a treatment career involving multiple treatment episodes may ultimately facilitate progression to stability and recovery (Anglin, Hser, & Grella, 1997). Other researchers, however, have demonstrated the limits of repeat attempts at methadone maintenance (Strike et al., 2005) finding that second and third admissions led to less likelihood of stability and recovery. We were unable to assess the role of repeat admissions to methadone treatment; for most patients in the CBMMP, this was their first experience with methadone maintenance. 4.5. Limitations One limitation of the study is that, for patients transferred in from a private clinic (42%), ASI information was obtained for the period before starting methadone at that clinic. The reliability and validity of ASI information obtained in this way are unknown. However, we observed that program transfer was not associated with involuntary and voluntary discharges at the univariate level and when this variable was forced in both multivariable models, all estimates were similar (data not shown). Another limitation is the possible social desirability bias to report less drug use. However, given the use of supervised random urine toxicology screening tests and the fact that the same clinic personnel were involved in administering the ASI and urine screening, a large discrepancy between self-reported and actual drug use is thought to be minimal. Previous studies have also shown agreement between self-reported drug use and urinalysis (Weatherby et al., 1994). In addition, our findings are limited by the information and variables available (ASI data). By not including measures of treatment satisfaction and quality of life, we were not able to appreciate the full range of possibly important determinants for patient retention. In addition, by using baseline versus time updated information, temporal associations are weakened. On the whole, however, these findings could be generalized to similar medium threshold programs in small communities where harmful opioid analgesic use is a problem. 4.6. Conclusion The CBMMP has demonstrated in a very short time that patients can be retained in treatment. We have also begun to observe other successes: as of May 2010, approximately 10% of patients were in

the process of voluntarily tapering methadone with a view to program completion. Patient retention is an important goal of MMT programs and unplanned exits often lead to a relapse to injection drug use and other high-risk behaviours. By breaking down patient loss to followup, we discovered that patients failing MMT are not homogenous. Understanding which patient characteristics are associated with program discharge and withdrawal may help MMT program administrators and personnel in improving patient retention. For example, efforts to avoid the involuntary discharge of patients might focus on additional supports for female patients with a longer lifetime history of opioid analgesic use and male patients reporting sedative abuse. In addition, for patients having attributes consistent with more severe drug dependence (e.g., current injection drug use), efforts to prevent voluntary discharge might employ a focused consideration of the injection act and patients' views regarding the ritual and the importance of this in their lives. In addition, an active exploration of the reasons why females choose to leave treatment needs to be undertaken. Our examination of the role of baseline personal characteristics in program retention could serve to stimulate a similar consideration of retention outcomes in urban centers. Opioid analgesic use is increasingly observed in centers traditionally affected by widespread heroin abuse (Leclerc et al., 2011). Because there is evidence to suggest opioid analgesic users may greatly differ from heroin users, knowledge of the specific determinants may be important for treatment retention (Wu et al., 2011). Our study also demonstrated the importance of the vulnerable domains of the Gelberg–Andersen Behavioral Model in considering predictors of methadone maintenance loss to follow-up. Optimizing retention could be guided by what we have learned from the traditional (e.g., sex) and vulnerable domains (e.g., years of drug use) as well as a wide range of program-related (e.g., methadone dosing and contingency management policies) (Faggiano et al., 2008; Table 4 Cox proportional hazards multivariate regression of patient characteristics (predisposing) relative to voluntary discharge; n = 204, CBMMP, Canada, May 2004–May 2007. Variables

Voluntary discharge aHR (95% CI)

Predisposing Sex Injecting opioids Use of sedativesa

2.47 (1.11–5.54) 4.44 (1.64–11.99) 3.69 (1.61–8.47)

Abbreviations: aHR = adjusted hazard ratio, CI = confidence interval a Refers to past 30 days.

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Peles et al., 2011) and patient-related (e.g., treatment motivation) factors (Simpson et al., 1997). By following these outcomes (i.e., involuntary and voluntary discharges) and by incorporating patientidentified treatment needs and objectives as well as information on program attributes, a more comprehensive appreciation of the effectiveness of MMT programs may be known.

Acknowledgments J. Cox wishes to dedicate this work to his parents, John and Dora Cox. Funding for this study was provided by Addiction Services, CBDHA, and the Nova Scotia Department of Health. The authors thank the program participants and personnel of Addiction Services, CBDHA, Nova Scotia Department of Health. The authors wish to recognize the important work of the staff of the CBMMP, now known as the Opiate Recovery Program of Mental Health & Addiction Services, CBDHA: Sharon MacKenzie, manager; Barry MacNeil, manager of community based services; and the nurses who work diligently to meet the needs of patients and other professionals challenged with understanding the program and its objectives. In addition, this program was possible through the commitment and support of several directors of Addiction Services (Everett Harris, Wayne York and Dr. Linda Courey) and of Dr. M. Naqvi, Medical Director, Cape Breton Regional Hospital and J. Malcolm, Chief Operating Officer, CBDHA, Nova Scotia Department of Health. Finally, the authors acknowledge the statistical assistance provided by Garbis Meshefedjian.

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