Efficacy and tolerability of antidepressants in the treatment of ...

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Efficacy and tolerability of antidepressants in the treatment of adolescents and young adults with depression and substance use disorders: a systematic review and meta-analysis Xinyu Zhou1*, Bin Qin1*, Cinzia Del Giovane2*, Junxi Pan1*, Salvatore Gentile3, Yiyun Liu1, Xinghui Lan4, Jia Yu1 & Peng Xie1 Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China,1 Department of Clinical and Diagnostic Medicine and Public Health-Italian Cochrane Centre, University of Modena and Reggio Emilia, Modena, Italy,2 Department of Mental Health, ASL Salerno Mental Health Center, Salerno, Italy3 and Department of Neurology, Children’s Hospital of Chongqing Medical University, Chongqing, China4

ABSTRACT Aims To measure the effectiveness of antidepressants for adolescents and young adults with co-occurring depression and substance use disorder. Design, Setting and Participants Meta-analysis of randomized controlled clinical trials. A comprehensive literature search of PubMed, Cochrane, Embase, Web of Science and PsychINFO was conducted (from 1970 to 2013). Prospective, parallel groups, double-blind, controlled trials with random assignment to an antidepressant or placebo on young patients (age ≤ 25 years) who met diagnostic criteria of both substance use and unipolar depressive disorder were included. Five trials were selected for this analysis and included 290 patients. Measurements Our efficacy outcome measures were depression outcomes (dichotomous and continuous measures) and substance-use outcomes (change of frequency or quantity of substance-use). Secondary analysis was conducted to access the tolerability of antidepressant treatment. Findings For dichotomous depression outcome, antidepressants group was significantly more effective than placebo group [risk ratio (RR) = 1.21; 95% confidence interval (CI) 1.01–1.45], with low heterogeneity (I2 = 0%). Although no statistically significant effects for continuous depression outcome [standardized mean differences (SMD) = −0.13; 95% CI, −0.55 to 0.30] were found with moderate heterogeneity (I2 = 63%), subgroup analysis showed that the medicine group with a sample size of more than 50 showed statistically significant efficacy compared with the placebo group (SMD −0.53, 95% CI −0.82 to −0.25). Moreover, there was no significant difference for substance-use outcomes and tolerability outcomes between the medication and placebo groups. Conclusions Antidepressant medication has a small overall effect in reducing depression in young patients with combined depressive and substance-use disorders, but does not appear to improve substance use outcomes. Keywords atic review.

adolescents, antidepressants, major depressive disorder, meta-analysis, substance use disorders, system-

Correspondence to: Peng Xie, Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, 1 Yixueyuan Road, Yuzhong District, Chongqing 400016, China. E-mail: [email protected] Submitted 23 February 2014; initial review completed 1 May 2014; final version accepted 25 July 2014

INTRODUCTION Depression, together with substance use disorders (SUDs), are both the most common mental illnesses in adolescents and young adults [1]. Both disorders tend to co-occur increasingly, accounting for about threequarters of the burden of all mental illness in this age

group [2]. Compared with adults, young people are more likely to drink alcohol, use marijuana and misuse medications [3], with an estimated 20–30% of depressed adolescents suffering from at least one comorbid SUD [4]. As reported in several studies, substance use disorders can either be the cause or the consequence of mood disorders [5], and their co-occurrance is also associated with more

*X.Z., B.Q., C.D.G. and J.P. contributed equally to this systematic review. © 2014 Society for the Study of Addiction

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pronounced psychosocial dysfunction, poorer drug treatment outcomes and higher risk of suicide [6-9]. Unfortunately, there are scarce data available regarding the effectiveness of pharmacological interventions in depressed youths with comorbid SUD, despite the widespread prevalence and severity of this combination of illnesses. Indeed, these comorbid populations are often excluded from clinical trials investigating the effectiveness of antidepressant drugs [10,11]. Given the paucity of research, clinicians are often reluctant to prescribe antidepressants for depressed young people with SUD. Commonly, such patients are first referred to and expected to complete substance treatment and achieve a sustained period of abstinence before antidepressant medication is considered; however, successful treatment of SUD is less likely if depression is not treated [12]. Moreover, observational studies tend to suggest that co-occurring depression is associated with worse treatment outcome for SUD [13,14]. Thus, the major controversy is whether to treat pharmacologically these depressed youths with ongoing substance abuse. One previous meta-analysis of depressed adults with SUDs showed that antidepressant medication may exert modest significant beneficial effects [15]. Another meta-analysis of adult patients also showed that antidepressants would significantly improve depressive symptoms in depressed patients with alcohol dependence [16]. However, evidence is needed to help guide treatment in these younger patients, because the risk factors and management of depression differ from those of adults and thus require specific guidelines [17,18]. Therefore, by following closely the designed format and statistical methods of one previous similar metaanalysis on adults [15], we undertook a systematic review and meta-analysis of placebo-controlled trials to evaluate the response to antidepressants of depressive symptoms at onset in substance abuse patients and the impact of this treatment on the course of concurrent SUD in adolescents and young adults.

METHODS Data sources and searches PubMed, Cochrane, Embase, Web of Science and PsychINFO databases, as well as websites of clinicaltrials.gov and fda.gov from 1970 to December 2013, were searched using the keywords ‘antidepressant treatment’ or ‘treatment depressed’ and ‘adolescents’ or ‘young’, in conjunction with each of the following words: substance use, drug abuse, alcohol use, benzodiazepine use, opiate/opioid use, cocaine use, marijuana use or cannabis use, methadone use, barbiturates use and amphetamine use. Additional trials were obtained by scanning the © 2014 Society for the Study of Addiction


reference lists of all identified related publications and related reviews. The relevant principal manufacturers and study authors were contacted to supplement incomplete original reports or to provide new data from unpublished studies. No language restrictions were applied. Two authors (B.Q. and Y.Y.L.) independently screened the titles and abstracts of each citation, identified randomized controlled trials (RCTs) involving adolescent and young adult patients with both substance use and depression and applied the inclusion criteria described below, and any disagreements were solved via discussion with another member of the reviewing team (X.Y.Z.). Selection criteria Studies were included if they met the following criteria: (i) prospective, parallel groups, controlled clinical trial with random assignment to an antidepressant medication or placebo; (ii) we focused on trials entering adolescents and young adults (aged ≤ 25 years) with current unipolar depressive disorders; (iii) patients met standard diagnostic criteria set forth in the Diagnostic and Statistical Manual of Mental Disorders 3rd, revised 3rd or 4th editions (DSM-III [19], DSM-III-R [20], DSM-IV [21]) for both a current substance (alcohol, opiate, cocaine, cannabis, benzodiazepine or other stimulant) disorder and a current major depressive disorder or other depressive disorder, diagnosed by clinical or structured diagnostic interviews; and (iv) the outcome of depressive symptoms is reported. We excluded trials with duplicate secondary analyses, bipolar depression or where we were unable to extract any data. Outcome measures For the analysis of effectiveness, we considered 12-week treatment studies to be acute treatment. If 12-week data were not available, we analysed data from 8- to 16-week studies (we gave preference to the time-point given in the original study as the study end-point) [22]. We accessed both the dichotomous and continuous measures for depression outcomes in this meta-analysis. Dichotomous depression outcome with response was defined as ≥50% reduction in depression rating scales [such as the Childhood Rating Depression Scale revised version (CRDS-R) [23] or Hamilton depression rating scale (HAMD) [24]] scores from the baseline to end-point, and with remission as CRDS-R ≤ 28 or HAM-D ≤ 7 [25]. In addition, we compiled continuous measures of depression scale scores, reflecting the effect size with standardized difference between means from baseline to end-point. When data were reported on both the CRDS-R and HAMD, we included data from the CRDS-R, as it was the most commonly used measure of depressive symptoms in young patients. Addiction, 110, 38–48


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The substance-use outcomes consisted of continuous measures of frequency or quantity of use, collected via the time-line follow-back method (TLFB) [26] or other measurement instruments. The standardized differences between means were calculated from change means from baseline to end-point. When several such measures were reported within a study, the effect sizes were averaged to obtain a summary effect. Tolerability outcomes were assessed by the completion of trial protocol, discontinuation for adverse events and suicide-related outcome. We defined completion of trial protocol as the number of patients who completed the study at the end of treatment, out of the number of patients who were assigned randomly. The discontinuation for adverse events was defined as the number of patients who dropped out of the study due to adverse events. Suicide-related outcome was defined as the proportion of patients who were recorded as one or more events with definite suicidal behaviour or ideation [27]. Data extraction and quality assessment Two authors (B.Q. and Y.Y.L.) read each article independently and assessed the completeness of the data abstraction. We used a structured data abstraction form to ensure consistency of appraisal for every study. Data extraction forms were sent to the original authors of the trial reports when necessary, with a request to provide missing data and the option to make corrections. Missing dichotomous measures were estimated from baseline scores, end-point means and standard deviations by a validated imputation method [28]. Intention-to-treat (ITT) data sets were used whenever available. Study quality was assessed by the Cochrane Collaboration’s risk-of-bias method [29]. Statistical analysis Effect sizes and pooled estimates of effect across studies were calculated with the RevMan5 software (Cochrane Information Management System), using analysis of inverse variance models for risk ratios (RR) in dichotomous measure and inverse variance models for standardized mean differences (SMD) in continuous measure, as well as 95% confidence intervals (CIs). We decided to use a random-effects model, as there was expected clinical diversity in the antidepressant medications, given their differing actions on various substance use disorders. Heterogeneity was evaluated with the Q statistic and the I2 statistic, a transformation of Q that estimates the percentage of the variation in effect sizes due to heterogeneity [30]. A probability value of ≤0.10 was taken as statistically significant, and an I2 of 30, 50 and 75% represented low, moderate and substantial heterogeneity, respectively. Considering the possibility that effectiveness may differ © 2014 Society for the Study of Addiction

according to the moderator variables for each trial, we conducted various subgroup analyses of the patients’ characteristics and design features, including substance type, placebo response (proportion of patients achieving response or remission criteria in the placebo group), sample demographics (percentage of female, age range, sample size) and trial features (type of depression rating scale, class of antidepressant medication, whether concurrent with psychosocial treatment). Moreover, we performed sensitivity analyses of excluding studies with imputation response data and excluding high risk of bias studies. Inverted funnel plots and Egger statistical tests were used to assess the potential presence of publication bias [31]. The protocol of the systemic review followed the recommendations for conducting a meta-analysis. All tests were two-sided, and statistical significance was defined as a probability value of 15 14–25 38.6

Cornelius, 2010 [35] Cannabis, 34/36 alcohol Deas, 2000 [36] Alcohol 5/5

17.6 ± 6.8


70.8% versus 65.4% 4.54 ± 7.06 versus 8.31 ± 8.97 73.5% versus 55.6% 6.06 ± 4.66 versus 7.0 ± 8.24 40% versus 80% 12.0 ± 4.95 versus 10.4 ± 3.65 50% versus 50% 34.6 ± 13.7 versus 31.3 ± 13.7 69.8% versus 52.4% 26.0 ± 11.7 versus 30.6 ± 11.6 CBT and MET CBT and MET CBT 12 Fluoxetine, 10–20 20.0 ± 8.5 HAMD DSM-IV, HAMD > 15 15–20 56.1

Treatment Concurrent Response or (weeks) therapy remission rates Age Female Depression Baseline score Medication, (years) (%) scale (mean ± SD) (mg/day)

24/26 Cornelius, 2009 [34] Alcohol

Data on depression outcome are summarized in Fig. 2. The medication group was associated with a significant increase of 21% in dichotomous depression outcome with response or remission rate compared with the placebo group. The value of RR from the random-effects model is 1.21 [95% confidence interval (CI) = 1.01– 1.45] with low heterogeneity among the studies (P = 0.47, I2 = 0%). For the continuous depression outcome with changes in the depression scale score from baseline to end-point, the pooled SMD between medication and placebo groups is −0.13 (95% CI = −0.55 to 0.30), with a moderate significant heterogeneity (P = 0.03, I2 = 63%). Due to significant heterogeneity in continuous depression outcome, we conducted various subgroup analyses to detect potential bias (Table 2). Sample size is a powerful predictor of antidepressant effect, explaining 99.6% of the variance

Diagnostic Patie-ntsa criteria

Meta-analysis results for depression outcomes

Substance use

being randomized, double-blind and placebo-controlled, the designs of the included trials were uniform in using DSM-IV criteria for diagnosing major depressive disorders; in providing adequate medication dosages; and in having trial durations from 8 to 16 weeks (median 12), which is sufficient time for antidepressant effects to occur. Outcomes on both the depression outcomes and completion of trial protocol were determined on all randomized patients, so that 290 patients were evaluated. Data for other outcomes (substance-use outcomes and suiciderelated outcome) were not available in several studies, resulting in smaller overall numbers of patients. The missing dichotomous measure in Cornelius et al.’s study was imputed from continuous measure [38].


Figure 1 Literature search

Table 1 Patients’ characteristics and main measures of placebo-controlled trials of antidepressants in substance use patients with major depressive disorders.

Depression scale scores (mean ± SD)

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Figure 2 Meta-analysis results of antidepressant medication: treatment effects on depression outcomes. (a) Comparison of antidepressant medication versus placebo for dichotomous outcome: response or remission patients. (b) Comparison of antidepressant medication versus placebo for continuous outcome: change scores in depression rating scales

in effect sizes across studies and eliminating variance due to heterogeneity; in the medicine group, with a sample size of more than 50, antidepressants showed a statistically significant efficacy compared with the placebo group (SMD = −0.53, 95% CI = −0.82 to −0.25). This difference disappeared (SMD = 0.30, 95% CI = −0.11 to 0.71) in studies where medicated groups included fewer than 50 patients. Although other subgroups were non-significant moderators, four explained more than 15% of the variance. High response rates to placebo, studies on patients with alcohol-use disorder, a small proportion of females in the sample and lack of psychosocial intervention were associated with a trend towards smaller effect sizes. Meta-analysis results for substance-use outcomes Substance-use outcome data are summarized in Fig. 3. For frequency of use, there was no statistically significant difference of overall effect size between two groups. The value of SMD was 0.10 (95% CI = −0.15 to 0.34). Heterogeneity among the studies was statistically low (P = 0.99, I2 = 0%). However, a large placebo response on frequency of use would lead to so much improvement in both groups that no differences could be detected. In addition, inclusion in the medicated group was not associated with a significant decrease in the quantity of use (SMD = 0.21, 95% CI = −0.14 to 0.55), and heterogeneity was similarly low (P = 0.70; I2 = 0%), while there was also a large placebo response on the quantity of use. © 2014 Society for the Study of Addiction

Meta-analysis results for tolerability and suicide-related outcome Data on completion of the trial protocol would reflect the overall tolerability. We found that there was no statistical difference between the medication and placebo groups in completion of trial protocol (Fig. 4a). The RR was 0.99 (95% CI = 0.94–1.04), with low heterogeneity (P = 0.36, I2 = 0%). For discontinuation for adverse events there was also no significant difference, with RR of 0.89 (95% CI = 0.06–13.08). Suicide-related outcomes had a nonsignificant result, with RR of 2.21 (95% CI = 0.41– 11.95) with low heterogeneity (P = 0.39, I2 = 0%), while the suicide rate of the medication group (4.76%) was more than that of the placebo group (1.90%) (Fig. 4b). However, the wide confidence interval from very small trials suggests limited power.

Quality assessment and publication bias Most studies were rated as low risk with respect to concealment of treatment allocation, method of generating the sequence of treatment allocation, blinding of participants and personnel and blinding of outcome assessment. Only two were rated as unclear, with allocation concealment [41] or blinding [40], respectively, and one trial reported high risk on selective outcome reporting [39]. However, after a sensitivity analysis for low risk of bias studies, there was no substantive change and the overall study quality ratings were consistently high. In addition, we inspected the inverted funnel plots of these Addiction, 110, 38–48

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0.29 (−0.22 to 0.80) −0.32 (−0.78 to 0.14) −0.32 (−0.78 to 0.14) 0.29 (−0.22 to 0.80)

0.34 (−0.26 to 0.93) −0.30 (−0.77 to 0.17) −0.06 (−0.74 to 0.62) −0.16 (−0.97 to 0.66) 0.30 (−0.11 to 0.71) −0.53 (−0.82 to −0.25)

−0.17 (−0.62 to 0.29) 0.36 (−0.90 to 1.61) −0.24 (−0.68 to 0.21) 0.33 (−0.35 to 1.01) −0.06 (−0.72 to 0.59) −0.14 (−0.97 to 0.68)

3 studies (n = 115) 2 studies (n = 29)

2 studies (n = 23) 3 studies (n = 121) 3 studies (n = 86) 2 studies (n = 58) 3 studies (n = 47) 2 studies (n = 97)

4 studies (n = 139) 1 study (n = 5) 4 studies (n = 126) 1 study (n = 18) 3 studies (n = 63) 2 studies (n = 81)

SMD (95% CI)b

2 studies (n = 29) 3 studies (n = 115)

No. of studies (patients)

5.612 4.701

7.505 –

9.873 –

0.032 0.021

5.802 4.901

0.001 6.332

5.282 0.011

0.011 5.282

Qdf within


0.06 0.03

0.06 –

0.02 –

0.99 0.89

0.06 0.03

0.97 0.04

0.07 0.90

0.90 0.07


64 79

60 –

70 –

0 0

65 80

0 68

62 0

0 62

% Variance explainedc









Q between

Effect of subgroup










30 Age range Adolescents Adolescents and young adults Sample size ≤ 50 >50 Trial features Medication type Fluoxetine Sertraline Concurrent Psychotherapy Yes No Rating scale HAMD CRDS-R



Table 2 Subgroup meta-analysis results of effect of antidepressant medication treatment effects on the continuous depression outcome.

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Figure 3 Meta-analysis results of antidepressant medication: treatment effects on substance-use outcome. (a) Comparison of antidepressant medication versus placebo for substance-outcome: the frequency of addition. (b) Comparison of antidepressant medication versus placebo for substance-outcome: the quantity of use

Figure 4 Meta-analysis results of antidepressant medication: treatment effects on tolerability and suicide-related outcome. (a) Comparison of antidepressant medication versus placebo for tolerability outcome: the completion of trial protocol. (b) Comparison of antidepressant medication versus placebo for suicide-related outcome outcome: patients with suicidal behaviour or ideation

studies visually, which appeared to be approximately symmetrical. Because the total number of studies was too small to show clear asymmetry, we performed the Egger test and the results showed that the depression outcomes (t = 1.86, P = 0.16) were not influenced by publication bias. Sensitivity analysis models the robustness of the pooled effect size with respect to imputation response data as a function of parameters reflecting different patterns of selection bias. After excluding the trials with © 2014 Society for the Study of Addiction

imputation response patients, the pooled estimate of effect size was 1.11, with low heterogeneity. None the less, caution is suggested in explaining the bias, because of the considerable number of imputation studies.

DISCUSSION Overall, this meta-analysis finds antidepressant medication effective for the treatment of depressive syndromes Addiction, 110, 38–48

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in adolescent and young adult patients with SUD, but at present the evidence does not support that it will help the addiction. In our review, the effect size of antidepressant medication for response or remission (RR = 1.21; 95% CI = 1.01–1.45) was similar (RR = 1.18; 95% CI = 1.08–1.28) to that which emerged from a recent meta-analysis of antidepressant trials in children and adolescent depressed patients [43]. Despite significant heterogeneity in continuous depression outcome, its effect was related strongly to the sample size. Studies with a large patient size yielded a significant antidepressant effect, while small-sized studies did not. None the less, the combined effect size must be interpreted with caution in the setting of the small number of reviewed trials. We found no previous review of antidepressant trials in young patients with substance abuse disorder, but one meta-analysis [15], reporting treatment of depression in adult patients with alcohol or other drug dependence, also showed a significant beneficial effect of antidepressant medications. In our review, our findings suggest that there was no significant difference in improving substance abuse symptoms, including both frequency and quantity of substance use, between antidepressant and placebo groups. Therefore, emphasis needs to be placed on treatment of the addiction, independent of antidepressant treatment. However, antidepressants might be effective, because a smaller effect size is unlikely to be detected with this small sample. The previous meta-analysis on adults also showed that trials with significant medication effects were more likely to show significant effects on substance use outcome [15]. Furthermore, substance use outcome effects could be hard to detect if there was a large placebo response on the substance outcomes (so much improvement in both groups that no differences could be detected). Conversely, SSRI antidepressants, e.g. fluoxetine and sertraline, have been associated with worse alcohol use outcome, compared to placebo, in several trials in adults (not selected for depression) in the subgroups with early-onset alcoholism [44,45]. Here the standardized mean difference favoured placebo over medication for substance use outcomes (although differences were non-significant). Thus, among such dually diagnosed young patients, further research is needed to determine whether antidepressants may impact upon addiction-related outcomes. With regard to the tolerability measures, completion of trial protocol and suicide-related outcome, we found no significant difference between the medicated and placebo groups. The effect size (RR = 0.99; 95% CI = 0.94–1.04) of completion of the trial protocol in our review was very similar to that (RR = 0.99; 95% CI = 0.94–1.05) reported in a previous meta-analysis in children and adolescent depressed patients [43]. © 2014 Society for the Study of Addiction


However, the absence of evidence for worse tolerability of antidepressants in this analysis is reassuring, because the sample size is clearly too small to reach firm conclusions. Conversely, in our findings, although the confidence limits are very wide and not statistically significant, the odds ratio was greater than 2, which would seem to be clinically significant. The US Food and Drug Administration (FDA) issued a public warning in October 2004 about an increased risk of suicidal thoughts or behaviour (suicidality) in children and adolescents treated with SSRI antidepressant medications [46]. In 2006, an advisory committee to the FDA recommended extending the warning to include young adults up to age 25 years [47]. None the less, the United Kingdom’s Medicines and Healthcare Products Regulatory Agency (MHRA) has contraindicated the use of all SSRIs other than fluoxetine in younger patients, with only fluoxetine deemed to have a positive balance of risks versus benefits [15]. Also, fluoxetine is the only drug approved in Germany for the treatment of depressive disorders in children and adolescents aged 8 years and above [48]. In our review, sample size is the most powerful predictor of antidepressant effect among all subgroup analyses. For depressive symptoms score outcome, the trials undergoing antidepressant medication with sample sizes ≥50 had a significantly beneficial outcome. Some statisticians demonstrated that a small sample size may result in an increase in the variability of results by larger standard deviation [49] and affect the reliability of a survey’s results, because it leads to a higher bias, obscuring the effects of medication [50]. It has often been noted by methodologists and authors of systematic reviews that many effects have been missed due to the lack of study planning and thus having a low sample [51]. However, it should be noted that, given the small sample of studies, interpretation of these subgroup analyses was limited.

Strengths and limitations Treatment studies on comorbidity are difficult to carry out because of inclusion criteria requiring two current disorders, especially for minor patients. Hence, given the small number of studies, and relatively small samples of the studies themselves, caution is needed in interpreting the results. Moreover, this meta-analysis was also facilitated by relatively consistent methods and quality indicators across studies, as well as a limited risk of publication bias detected by the Egger test and sensitivity analysis. Depression in substance abuse patients often co-occurs with other disorders [e.g. post-traumatic stress disorder (PTSD) [52] and other anxiety disorders [53]]. This (lack of measurement of other disorders) should be viewed as a limitation, as those other disorders may be more important in driving both low mood and substance Addiction, 110, 38–48


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use. In addition, the moderators were identified retrospectively so that unmeasured confounders could explain the findings, which may be a potential bias [16]. Thus, future studies should isolate and examine factors such as substance-use type and gender type co-occurring with other disorders and concurrent psychosocial interventions, probably requiring larger sample sizes and multisite designs.

CONCLUSIONS This meta-analysis suggests that, in these young patients, alcohol or drug abuse should not be considered as an obstacle to the use of antidepressant medications on improving depression symptoms, while there is no evidence that antidepressant treatment improves substance use outcome from these data. The use of antidepressants in alcohol or drug abuse with comorbid depression in adolescents needs more studies in well-defined samples, adequate doses and duration of treatment to be really conclusive. Differences related to both individual characteristics and specific antidepressant drugs need to be clarified in future studies. Declaration of interests None. Acknowledgements The study was funded by the National Basic Research Program of China (973 Program, Grant No.2009CB918300) and Natural Science Foundation Project of China (CSTC, 31271189).The authors thank Anne-Liis von Knorring and Kurt Michael for their comments on an early draft of the manuscript. References 1. Kaminer Y., Connor D. F., Curry J. F. Comorbid adolescent substance use and major depressive disorders: a review. Psychiatry (Edgmont) 2007; 4: 32–43. 2. Andrews G., Wilkinson D. D. Prevention of mental disorders in young people. Med J Aust 2002; 177: S97–100. 3. Sawyer M. G., Arney F. M., Baghurst P. A., Clark J. J., Graetz B. W., Kosky R. J. et al. Mental Health of Young People in Australia: Child and Adolescent Component of the National Survey of Mental Health and Wellbeing. Mental Health and Special Programs Branch, Commonwealth Department of Health and Aged Care. 2000. Available at: http://www.health .gov.au/internet/publications/publishing.nsf/Content/ mental-pubs-m-young-toc (accessed 10 September 2013) (Archived at http://www.webcitation.org/6RxUXLkXm on 19 August 2014). 4. Birmaher B., Ryan N. D., Williamson D. E., Brent D. A., Kaufman J., Dahl R. E. et al. Childhood and adolescent depression: a review of the past 10 years. Part I. J Am Acad Child Adolesc Psychiatry 1996; 35: 1427–39. © 2014 Society for the Study of Addiction

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