امکان سنجی محاکمه مداخلات شناختی و رفتاری برای افسردگی در بیماران دسترسی به درمان مواد مخدر و الکل تصادفی کنترل شده

Depressed mood often co-exists with frequent drug and alcohol use. This trial examined the feasibility of screening, recruitment, randomization and engagement of drug and alcohol users in psychological interventions for depression symptoms. A total of 50 patients involved in community drugs and alcohol treatment (CDAT) were randomly allocated to behavioral activation delivered by psychological therapists (n = 23) or to cognitive behavioral therapy based self-help introduced by CDAT workers (n = 27). We examined recruitment and engagement rates, as well as changes in depression (PHQ-9) symptoms and changes in percent days abstinent (PDA within last month) at 24 weeks follow-up. The ratio of screened to recruited participants was 4 to 1, and the randomization schedule successfully generated 2 groups with comparable characteristics. Follow-up was possible with 78% of participants post-treatment. Overall engagement in psychological interventions was low; only 42% of randomized participants attended at least 1 therapy session. Patients offered therapy appointments co-located in CDAT clinics were more likely to engage with treatment (odds ratio = 7.14, p = .04) compared to those offered appointments in community psychological care clinics. Intention-to-treat analyses indicated no significant between-group differences at follow-up in mean PHQ-9 change scores (p = .59) or in PDA (p = .08). Overall, it was feasible to conduct a pragmatic trial within busy CDAT services, maximizing external validity of study results. Moderate and comparable improvements in depression symptoms over time were observed for participants in both treatment groups.

There is considerable evidence that common mental health problems like depression and anxiety often co-occur with problematic alcohol and drug use (Marsden et al., 2000, Strathdee et al., 2002 and Weaver et al., 2003). People who frequently use substances are 2 times at greater risk of having a comorbid depression or anxiety disorder, and this increases to 5 times greater risk for dependent substance users (Merikangas et al., 1998). This combination of problems often complicates treatment and can result in greater functional impairment (Johnson et al., 1995), reduced treatment adherence (Carroll et al., 1993 and Ford et al., 1991), poor health outcomes (Hasin et al., 2002 and McKay et al., 2002) and increased risk of suicide (Harris & Barraclough, 1997). The detection of such comorbid disorders has historically been inconsistent in routine treatment in the United Kingdom (Weaver et al., 2003). Consequently, it has been estimated that only 1 in 5 people (20%) involved with community drugs services tend to access mental health treatment (Marsden et al., 2000). Even if comorbid mental health problems are adequately detected, treatment options for this client group seem to have fairly modest benefits. Pharmacological treatments for depression in alcohol and drug users appear to have mixed evidence, with some reviews that indicate a beneficial effect (Iovieno et al., 2011 and Nunes and Levin, 2004) and other reviews that question their efficacy (Lingford-Hughes et al., 2012, Pedrelli et al., 2011 and Torrens et al., 2005). In view of such evidence, exploring the potential of psychological treatments may be a fruitful avenue for research and practice. Published trials of psychological treatments for depression and anxiety in substance users suggest that cognitive behavioral therapy (CBT) may be an effective treatment (Baillie and Sannibale, 2007, Baker et al., 2010 and Brown et al., 2006; Brown, Evans, Miller, Burgess & Mueller, 1997; Hides et al., 2010, Hunter et al., 2012, Kay-Lambkin et al., 2009, Kay-Lambkin et al., 2011, Watkins et al., 2006 and Watkins et al., 2011). There is, however, scarce research on the application of contemporary behavioral activation (BA) models of treatment in clinical populations of substance users. BA is an intervention that alleviates depression by focusing primarily on changing maladaptive behaviors (such as avoidance, rumination, coping strategies that have unintended negative consequences) that are posited to maintain a cycle of low mood (Martell, Addis, & Jacobson, 2001). BA shares some conceptual underpinnings with CBT such as behavior modification and learning theories, but it does not emphasize the direct modification of thoughts and beliefs as in CBT. The efficacy of BA for the treatment of depression in adults has been endorsed by several meta-analyses of clinical trials (Cuijpers et al., 2007, Ekers et al., 2008, Ekers et al., 2014 and Mazzucchelli et al., 2009). However, to our knowledge, only one published controlled trial has tested the efficacy of BA with a clinical sample of dependent substance users (Daughters et al., 2008). This trial concluded that augmenting inpatient addiction treatment with BA leads to greater reductions in depression symptoms compared to usual inpatient care. Considering the prevalence and impact of common mental health problems in drug and alcohol users, and the emerging evidence-base for cognitive and behavioral interventions, we conducted a trial to investigate the feasibility of delivering BA and CBT based guided self-help for depression as part of routine community drugs and alcohol treatment (CDAT). Given our focus on feasibility, the study design also aimed to assess whether co-locating BA within CDAT clinics may enhance engagement with therapy, by comparison to offering this intervention in external mental health clinics as in usual practice. This aspect of the trial was informed by policy guidelines (Department of Health, 2002) that promote integration and close partnership work between substance use and mental health professionals. Although this seems like a sensible policy, we are not aware of empirical evidence specifically supporting the co-location of psychological interventions within CDAT settings and we therefore considered it worthy of further investigation.

3.1. Sample characteristics and feasibility As shown in Fig. 1, a total of 207 patients were approached for mental health screening during an 18 month period, based on their response to the TOP (item 4a) questionnaire which indicated that they potentially met criteria for a common mental disorder. More detailed screening using PHQ-9 was feasible with 186 patients (89.9% of 207), out of whom 73 (39.2% of screened cases) met criteria for the study but only 50 (68.5% of eligible cases) consented to participate. Based on these observations, we estimate that it is necessary to screen 4 patients to successfully recruit and randomize one consenting and eligible participant (207/50 = 4.14 = number needed to screen). Consenting participants were mostly white British (72.0%) males (68.0%), with a mean age of 37.2 (SD = 6.6), most of whom were currently prescribed opiate substitute medication (92.0%) and anti-depressants (64.0%). The most commonly used substances in this sample were alcohol (50.0%), heroin (34.0%), crack (22.0%) and cannabis (22.0%). Table 1 presents detailed sample characteristics and demonstrates that there were no significant differences between the BA (n = 23) and GSH (n = 27) groups in any of these characteristics, except for mean baseline SDS which appeared to be higher in the BA group; U(50) = 202.00, p = .03. Importantly, there were no significant differences in baseline PHQ-9 between those who provided follow-up data (mean = 16.72, SD = 4.48) and those who did not (mean = 17.91, SD = 3.75); t(48) = − 0.80, p = .42. There were no significant differences in mean PDA estimates between participants who were followed-up (mean = 0.38, SD = 0.38) and those who were lost to follow-up (mean = 0.19, SD = 0.24); t(26) = 1.96, p = .06. Overall, the randomization process successfully produced two groups with comparable baseline characteristics, and there was no evidence of bias introduced by cases lost to follow-up. Table 1. Sample characteristics and comparisons between randomly assigned groups. Full sample N = 50 (100%) BA n = 23 (46%) GSH n = 27 (54%) Test statistic p Demographics Males 34 (68.0) 18 (78.3) 16 (59.3) χ2(1) = 2.06 .15 Mean age (SD) 37.2 (6.6) 38.4 (6.2) 36.2 (6.8) t(48) = 1.23 .22 Ethnicity White British 36 (72.0) 16 (69.6) 20 (74.1) χ2(1) = 0.13 .72 Other 14 (28.0) 7 (30.4) 7 (25.9) Substances used in the last month Alcohol 25 (50.0) 14 (60.9) 11 (40.7) χ2(1) = 2.01 .16 Mean units per week (SD) 42.9 (56.4)a 53.9 (73.3)a 30.9 (30.0)a U(23) = 61.00 .76 Heroin 17 (34.0) 5 (21.7) 12 (44.4) χ2(1) = 2.85 .09 Mean g. per week (SD) .29 (.33)a .15 (.13)a .34 (.37)a U(16) = 32.50 .27 Crack 11 (22.0) 3 (13.0) 8 (29.6) χ2(1) = 1.99 .16 Mean g. per week (SD) .19 (.13)a .10 (.00)a .22 (.14)a U(11) = 19.50 .09 Cannabis 11 (22.0) 6 (26.1) 5 (18.5) χ2(1) = 0.42 .52 Mean spliffs per week (SD) 10.3 (9.4)a 7.7 (10.3)a 13.4 (8.1)a U(11) = 22.50 .17 Other substances 4 (8.0) 4 (17.4) 0 – Poly-substance use 18 (36.0) 7 (30.4) 11 (40.7) χ2(1) = 0.72 .40 Injecting 6 (13.6)b 3 (15.0)b 3 (12.5)b – Abstinent 9 (18.0) 4 (17.4) 5 (18.5) – Severity of dependence and psychological symptoms at screening SDS mean (SD) 6.1 (3.7) 7.3 (3.8) 5.1 (3.4) U(50) = 202.00 .03 PHQ-9 mean (SD) 16.9 (4.3) 17.61 (4.7) 16.4 (4.0) t(48) = − 0.95 .35 GAD-7 mean (SD) 11.9 (4.7) 12.3 (4.0) 11.6 (5.3) t(48) = 0.50 .62 TOP-4a mean (SD) 8.5 (3.5)b 8.7 (3.6) 8.3 (3.4)b t(47) = 0.39 .70 Treatment Mean no. weeks in treatment (SD) 195.3 (119.1)b 202.9 (126.5)b 189.3 (115.1)b t(43) = 0.38 .71 Using opiate substitute prescription 46 (92.0) 21 (91.3) 25 (92.6) – Using antidepressants 32 (64.0) 14 (60.9) 18 (66.7) χ2(1) = 0.18 .67 Engaged with trial interventionc 21 (42.0) 8 (34.8) 13 (48.1) χ2(1) = 0.91 .34 t = Student's t-test; U = Mann-Whitney U test; χ2 = chi-square test; – denotes missing estimates due to violation of test assumptions. a Estimates exclude abstainers from each substance. b Estimates exclude missing data. c Refers to participants who attended at least 1 session of allocated intervention. Table options Only 21 participants (42.0%) actually engaged with their allocated intervention (defined as attending at least one session). There were no significant differences in engagement between the BA (n = 8; 34.8%) and GSH (n = 13; 48.1%) groups; χ2(1) = 0.91, p = .34. Those who engaged with BA attended a mean number of 3.13 sessions (SD = 1.73, mode = 5). A closer examination of the group of BA participants that engaged in treatment revealed that those offered co-located care (n = 5; 62.5%) attended a higher mean number of total therapy sessions (mean = 4.20, SD = 1.10) compared to those offered parallel care (n = 3, 37.5%, mean = 1.33, SD = .58); however the small numbers did not allow us to apply formal tests of statistical significance. All of those who engaged with GSH had only 1 session (as per protocol), except for one participant who required 2 sessions to work through the self-help booklet due to obstacles with concentration and literacy. We explored potential predictors of engagement using multivariate logistic regression. The final logistic regression model reached through a two-step process of backward elimination of variables is presented in Table 2. According to this model, poly-substance users were significantly less likely to engage with therapy (odds ratio = .15, p = .02) and patients offered ‘co-located’ appointments in a CDAT clinic (who accessed GSH or co-located BA) were at least 7 times more likely to engage compared to participants offered BA in general primary care mental health clinics (odds ratio = 7.14, p = .04). Table 2. Step-wise logistic regression modelling strategy to identify predictors of engagementa with psychological interventions. Variable Step 1 Step 2 Pseudo R2 = .45 Pseudo R2 = .35 B SE β p B SE β p Constant − 23.59 28235.63 < .001 .99 − 1.42 1.15 .24 .22 Gender b 1.34 1.26 3.82 .29 .53 .82 1.70 .51 Age lowest quartile (≤ 32) .60 .77 Age quartile 2 (33–36) -.28 1.52 .76 .85 -.09 1.02 .92 .93 Age quartile 3 (37–43) 1.65 1.54 5.19 .28 .27 1.01 1.31 .79 Age quartile 4 (≥ 44) .99 1.45 2.69 .50 1.07 1.17 2.90 .36 Poly-substance use b − 2.35 1.18 .10 .04 − 1.90 .79 .15 .02 Modality b 3.47 1.73 31.98 .04 1.97 .98 7.14 .04 Psychological treatment group b 1.26 1.53 3.53 .41 Ethnicity b − 1.40 1.45 .25 .33 Opiate substitute treatment b 19.37 28235.63 258202165.3 .99 Baseline PHQ-9 .03 .14 1.03 .82 Baseline SDS .01 .15 1.01 .97 Baseline PDA 1.22 1.51 3.39 .42 a Engagement is defined as having accessed at least one session of the allocated intervention; step 1 entered all potential predictors of engagement, while step 2 presents a more parsimonious model in which non-significant predictors were removed by backward elimination. b Reference categories: gender = male; age = lowest quartile; poly-substance use = non poly-use; modality = parallel care; psychological treatment group = CBT guided self-help; ethnicity = white British; opiate substitute treatment = not using; β = odds ratio. T