Collaborative and Stepped Care for Mental Disorders

Results of a Cluster-Randomized Controlled Trial in Outpatient Care (The COMET Study)

Background: Collaborative and stepped care (CSC) models are recommended in guidelines because of their effectiveness in treating depression and anxiety disorders. The evidence for other mental disorders is, however, limited. The aim of this study was to evaluate the effectiveness of a collaborative and stepped care model (COMET) for patients with depressive, anxiety, somatoform, and/or alcohol-related disorders and related comorbidities in the routine care setting in Germany.

Methods: A prospective, cluster-randomized, controlled, parallel-group superiority trial was conducted; the subjects were patients in primary care practices. The primary endpoint was the change in mental health-related quality of life, assessed with the SF-36 Mental Health Component Summary Score (MCS) at 6 months in the intention-to-treat population. The secondary endpoints were symptom severity, remission, and response.

Results: Forty-one primary care offices were randomized either to COMET (n = 20) or treatment as usual (TAU, n = 21), and 615 patients were recruited (CSC: n = 307; TAU: n = 308). Data were available for 62% (COMET) and 55% (TAU) of the participants at 6 months. No significant group difference was found with respect to the primary endpoint (−1.96 ,95% confidence interval [−4.39; 0.48], p = 0.113) or any of the secondary endpoints.

Conclusion: We found no superiority of the COMET model for the mental disorders addressed. Methodological issues, including differences at baseline and high dropout rates, make these findings challenging to interpret. Future studies should ensure comparability of groups, allocate resources for quality management, and investigate more suitable outcome measures, paying attention to factors of implementation.

Cite this as: Heddaeus D, Seeralan T, Maehder K, Porzelt S, Daubmann A, Dams J, Grochtdreis T, König HH, von dem Knesebeck O, Löwe B, Pepić A, Rosenkranz M, Schäfer I, Zimmermann T, Schulte B, Weigel A, Wegscheider K, Werner S, Zapf A, Scherer M, Dirmaier J, Härter M: Collaborative and stepped care for mental disorders: Results of a cluster-randomized controlled trial in outpatient care (the COMET study). Dtsch Arztebl Int 2025; 122: 127–34. DOI: 10.3238/arztebl.m2025.0011

With a 12-month prevalence of 17.6% worldwide and 27.7% in Germany, mental disorders are a frequent phenomenon (1, 2). They impose a substantial burden on patients and healthcare, with high direct and indirect costs (3). Mental and substance use disorders are responsible for 22.8% of years lived with disability (4). The most prevalent are depression and anxiety, somatoform, and alcohol-related disorders (5), with significant symptom overlap. Indeed, 44% of patients report two and 22%, three or more comorbid mental disorders (2, 6).

Primary care is often the first point of contact for patients with mental disorders, yet referral to specialized care is difficult, even in highly structured healthcare systems (7, 8). Guidelines increasingly recommend collaborative and stepped care (CSC) as an appropriate model care provision, allowing efficient resource allocation and care for comorbid conditions (9, 10). CSC models are characterized by:

1) An interdisciplinary network of providers, including primary care physicians (PCP), mental health professionals (e.g., psychotherapists, psychiatrists), and nurses

2) Stepped care algorithms that allocate resources according to disorder severity and treatment response, based on systematic monitoring (11, 12).

Numerous studies have demonstrated the efficacy of CSC among patients with depressive and anxiety disorders (12, 13, 14). Promising evidence suggests that CSC improves the management of somatoform disorders (15, 16) and increases treatment uptake (e.g., psychosocial treatment and/or pharmacotherapy) in alcohol-related disorders (17). A systematic review of 39 studies found that only five of them addressed mental comorbidities, and just one investigated more than two mental disorders (18). Another CSC study on patients with anxiety, depressive and stress-related disorders found no significant differences compared to treatment as usual (TAU), but earlier treatment response in the CSC group (19).

The aim of the study presented here was evaluate the effectiveness of a guideline-based CSC model (Collaborative and Stepped Care in Mental Health by Overcoming Treatment Sector Barriers; COMET) in patients with depressive, anxiety, somatoform and/or alcohol-related disorders and their comorbidities under routine conditions of the German healthcare system (20). The primary hypothesis was that treatment with COMET leads to a greater improvement in mental health-related quality of life by 6 months after baseline than TAU. The secondary endpoints comprised changes in disorder-specific symptom severity, response rates, and remission rates, together with healthcare utilization.

Methods

Design

The study was a cluster-randomized, prospective, parallel-group superiority trial comparing the effectiveness of CSC (COMET) and TAU in primary care patients with depressive, anxiety, somatoform and/or alcohol-related disorders conducted in Hamburg, Germany (ClinicalTrials.gov ID: NCT03226743). The Hamburg Medical Chamber ethics committee approved the study protocol (PV5595). The results are reported in accordance with the CONSORT 2010 guidelines, and the protocol has been published (20). A detailed description of the methods is provided in the eMethods.

Inclusion and exclusion criteria

Clusters

For inclusion, primary care practices had to be licensed by the Association of Statutory Health Insurance Physicians in Hamburg and had to have signed a cooperation contract with the study group.

Patients

The inclusion criteria were age > 18 years, informed consent to study participation, and one or more of the following ICD-10 diagnoses: depressive episode (F32), recurrent depressive disorder (F33), dysthymia (F34.1), agoraphobia (F40.0), social phobia (F40.1), panic disorder (F41.0), generalized anxiety disorder (F41.1), mixed anxiety and depressive disorder (F41.2), somatoform disorders (F45), mental and behavioral disorders due to use of alcohol (F10). Patients with insufficient proficiency in German and those already receiving treatment were excluded.

Intervention

COMET

The intervention was a CSC program to be carried out by 20 PCP. The collaborative network comprised 20 psychotherapists (13 behavioral and 7 psychodynamic), four psychiatrists, and four inpatient or day-care facilities. An online scheduling platform was implemented to facilitate direct booking of outpatient psychotherapeutic and psychiatric appointments by PCPs. Network partners received training on evidence-based guidelines for depressive, anxiety, somatoform and alcohol-related disorders (9, 21, 22, 23, 24), and the COMET model. Furthermore, quarterly network meetings promoted quality assurance and information exchange. Further elements were computer-assisted and guideline-based treatment decision algorithms, including self-management interventions. The symptoms were monitored at disorder-specific intervals by the main care provider (PCP or mental health specialist). To identify severe cases with inadequate treatment, the study team tracked the care pathways of the patients concerned and notified the main care provider (see study protocol [20] and eSupplement 2, Table 1 for details).

Table 1

Baseline characteristics of the study groups

Treatment as usual

The comparison group received treatment as usual under the German statutory healthcare system, with unrestricted access to evidence-based mental health care in accordance with the clinical practice guidelines (e.g., S3 guidelines), which provide recommendations for diagnosis and treatment.

Endpoints

The primary endpoint was the change in mental health-related quality of life, assessed with the Short Form Health Survey (SF-36) mental component summary score (MCS) (25) from baseline to 6 months on a scale of 0 to 100. A general score was chosen due to the heterogeneous conditions. The secondary endpoints were change in the primary endpoint from baseline to 12 months, change in disorder-specific symptom severity from baseline to 6 and 12 months as measured by validated instruments (PHQ-9 [26], GAD-7 [27], PHQ-15 and PHQ-Panic module [28], SSD-12 [29], and AUDIT-C [30]) as well as disorder-specific response to treatment and remission (eMethods). Further secondary endpoints were physical health-related quality of life (SF-36 physical component summary score [PCS]), patient satisfaction (31), and treatment utilization. All endpoints were assessed through patient-reported questionnaires (32). Due to recruitment delays, two parameters from the protocol, the power and primary outcome measure, were modified (eMethods).

Data collection

Data were collected on a tablet using web-based software. Follow-up assessments were conducted via telephone interviews, with trained research assistants administering the Composite International Diagnostic Interview (CIDI) to verify the diagnoses and the exclusion criteria.

Statistical analyses

The sample size calculation aimed to detect a small to moderate standardized mean difference (Cohen’s d of ≥ 0.35) in the primary endpoint between COMET and TAU with 80% statistical power and a significance level of 5%. Accounting for anticipated dropout rates of 30% for practices and 20% for patients, we targeted the recruitment of 570 patients (285 per group) across 38 practices.

All data analyses were predetermined in a statistical analysis plan (eSupplement 1). The primary endpoint analysis was based on the intention-to-treat (ITT) population. A linear mixed model was applied with group (COMET/TAU), time (3, 6, and 12 months), and group by time interaction as fixed effects, with patients nested in practice as random effects and baseline values of the SF-36 MCS included as covariates. The analysis was repeated in the per-protocol (PP) population. Sensitivity analyses explored different imputation methods. Due to baseline imbalances, post hoc analyses included additional covariates and propensity score analyses. The secondary endpoints were analyzed using linear mixed models and mixed logistic regression. The subgroup analyses for the primary endpoint covered main diagnosis, comorbidity, sex, age, educational level, employment status, recruitment strategy, symptom severity, and intervention start during COVID-19 pandemic. Adjusted means, effect sizes, odds ratios, and their 95% confidence intervals are reported.

Results

Participants

The PCP were recruited in the period from 17 January 2018 to 31 December 2019, with invitation letters sent in two waves to n = 2451 PCPs in Hamburg. A total of 41 practices were recruited, with 20 randomized to the COMET and 21 to the TAU group (Figure 1). Seventeen COMET offices (52.9% female PCPs) and 14 TAU offices (71.4% female PCPs) actively included patients.

Figure 1

Flow chart showing the clusters and participants throughout the trial

Patients were recruited between 12 July 2018 and 22 October 2021. A total of n = 1183 (COMET: n = 504; TAU: n = 679) patients were screened for eligibility, with n = 713 (COMET: n = 347; TAU: n = 366) meeting the study diagnoses. Of those, n = 615 patients gave informed consent to participate (COMET = 307; TAU: n = 308).

The patients in the COMET and TAU groups differed in the primary endpoint (SF-36, MCS) and disorder severity at baseline, suggesting greater impairment among COMET patients (Table 1; eSupplement 2, Table 2). The baseline characteristics of dropouts did not differ from those of the participants who continued the study (eSupplement 2, Table 3). Further details on post-hoc sensitivity analyses, secondary endpoints, and subgroup analyses are provided in the eResults.

Table 2

Results of primary and secondary endpoint analysis as change from baseline to 6 months

Table 3

Results of analysis of secondary endpoints (binary) after 6 months

Effectiveness

Table 2 shows the results of the primary endpoint analysis with baseline-imputed data using a baseline-adjusted linear mixed model. No significant difference in MCS scores was found between the COMET and the TAU group (−1.96 [−4.39; 0.48], p = 0.113; Figure 2). Sensitivity analyses with different methods for missing values led to comparable results (eSupplement 2, Tables 4 and 5). The results of the primary analysis also proved to be robust in the post-hoc covariate-adjusted analysis (eSupplement 2, Table 6) and in the PP analysis (eSupplement 2, Table 7). Moreover, there were no group differences at 6 months in terms of secondary endpoints (Table 2), disorder-specific response, or remission (Table 3).

Figure 2

Results of the primary endpoint analysis

None of the subgroup analyses revealed relevant differences between the groups (eSupplement 2, Tables 12a and b). Descriptive analyses showed that COMET patients received psychotherapy (60% vs. 17%), psychopharmacotherapy (27% vs. 15%), and consultations with a psychiatrist (11% vs. 6%) more often than those in the TAU group (eSupplement 2, Table 13). Due to insufficient responses from the TAU group, data on adverse events are limited to 26 COMET patients, limiting interpretability (eResults).

Discussion

This cluster-randomized controlled trial evaluated the effectiveness of a guideline-based CSC model for patients with depressive, anxiety, somatoform or alcohol-related disorders and their comorbidities treated in a multiprofessional care provider network, compared with TAU. Under routine conditions of the German healthcare system and the design of the COMET study, no statistically significant group difference in mental health-related quality of life was observed from baseline to 6 months. Secondary analyses also showed no relevant differences. However, both groups showed clinical improvements in mental health-related quality of life over time, possibly reflecting high quality of routine care. Patients in the TAU group also underwent digital screening and diagnostic procedures, potentially enhancing both the detection of mental disorders and the care provided. Our results diverge from evidence in previous research suggesting the superiority of CSC models over routine care for patients with depression (33, 34) or anxiety disorders (12, 13), taking into account the heterogeneity in results, design and implementation of these models (13). Similar to our results, the CSC trial by Oosterbaan and colleagues (19) examined multiple conditions but did not find general superiority of the collaborative approach, yet reported significant differences in response and remission rates favoring the CSC group at 4 months.

Patient characteristics and selection

A major challenge in interpreting our results arose from baseline differences between the groups. Our qualitative survey indicated that PCPs in the COMET group may have selected more severely ill patients, anticipating easier access to specialized care. Differences in enrollment might also have been influenced by the support offered to PCPs in the TAU group in overcoming recruitment delays. The challenge of enrolling primary care patients in the TAU group and the risk of selection bias have been described in other studies (35). Consequently, despite cluster randomization, significant baseline differences emerged. Although the statistical analyses were adjusted for these imbalances, unobserved characteristics may still differ between groups and influence treatment response. Indeed, the propensity score plot and the baseline scores show that COMET patients appeared more burdened and possibly more severely ill, as shown by their lower MCS scores and higher symptom scores. Secondary analyses at 12 months showed differences in psychological burden related to somatic symptoms and depression-specific response favoring the COMET group. This delayed treatment response suggests that the benefits of the CSC model may not emerge until later in severely ill patients, a hypothesis to be examined in the pending 24-month follow-up analyses. In contrast to previous studies, which focused on single conditions, our trial included patients with multiple disorders and comborbidities, potentially complicating the response to treatment.

Implementation challenges

While the model was designed to be feasible for PCPs and mental health specialists, its implementation for patients with multiple disorders, alongside guideline training for network partners, may have been overly complex for routine care. The lack of rigorous monitoring of the response to treatment in the COMET model improved the systematic case-based communication among care providers only marginally.

Treatment delivery and quality

Descriptive analyses showed that COMET patients received more mental health treatments. However, this does not necessarily reflect the quality or appropriateness of treatment. Merely providing access to specialized care without enhancing collaboration may not be sufficient to improve outcomes (36), as highlighted by our qualitative survey of the care providers, which identified potential for improvement in communication, feedback loops and remuneration of collaborative activities (37). Further analyses are needed to examine how accurately the received treatments adhered to current guidelines and to determine which care pathways were implemented, particularly regarding the severity of disease (38).

Strengths and limitations

The COMET study is among the few to address multiple mental disorders and their comorbidities in a single model, conducted under German routine care conditions. Despite initial recruitment challenges, the targeted number of PCPs and patients was achieved. With sufficient statistical power and active engagement of PCPs, the trial’s implementation addressing comorbid mental health conditions highlighted its applicability in clinical practice. Furthermore, patient-reported outcome measures, cluster-randomization, and the blinded statistical analyses bolster the trial’s rigor, objectivity, and credibility.

Nonetheless, our trial is limited by methodical constraints. Alongside unequal baseline samples, we found high dropout rates of 38% (COMET) and 45% (TAU) at 6 months, comparable with previous studies (33). Notably, we found no differences between patients who continued treatment and those who dropped out. A significant proportion of patients were not captured between screening and the baseline telephone survey. While patient-reported endpoints, including health-related quality of life, are increasingly being used in efficacy trials (39), the SF-36 may lack the sensitivity required to detect subtle changes in mental health symptoms, particularly among populations with more severe conditions (40). Furthermore, the low response rates among the PCPs may limit external validity and generalizability, as those PCPs who participated may have been more open to mental health conditions, potentially affecting both groups equally. Lastly, the simultaneous implementation of study procedures and the CSC model complicated the evaluation by blurring start-up difficulties and assimilation processes with intervention assessment. Future studies should ensure group comparability, allocate resources for quality assurance, investigate additional outcome measures, and systematically analyze the determinants crucial for implementation. A comprehensive overview of the conclusions can be found in the eResults.

Data Sharing

The data supporting the findings of this study are available upon request from the corresponding author [MH] and are not readily accessible due to lack of permission from participants to share anonymized data publicly. Research materials associated with this study are also available from the corresponding author (MH), solely for the purposes of reproducing results or replicating procedures.

Funding

This study was funded by the German Federal Ministry of Education and Research (BMBF) under grant number 01GY1602. The sponsor had no influence on study design, data collection, analysis and interpretation of data, or manuscript preparation. It did approve changes to the study protocol recommended by the advisory board.

Acknowledgments

We would like to thank all care providers and patients who participated in this study. We are grateful to the members of the advisory board, Prof. Dr. Jürgen Unützer, Prof. Dr. Paul McCrone, Prof. Dr. Trudy van der Weijden, Prof. Eileen F. S. Kaner, Prof. Dr. Birgit Watzke, Prof. Dr. Wolfgang Hoffmann, and Prof. Dr. Michel Wensing, for their valuable contributions and suggestions and to Prof. Dr. Levente Kriston for methodological and biometrical consultation. Finally, we owe special thanks to all our student assistants for their enduring and reliable support in data collection.

Conflict of interest statement
BL has been the president of the German College of Psychosomatic Medicine (DKPM) (honorary) since March 2024 and was a member of the Board of the European Association of Psychosomatic Medicine (EAPM) (honorary) until 2022.

IS is the responsible coordinator of the German National Guidelines “Posttraumatic Stress Disorder” and “Opioid related Disorders”.

MH was chair of the scientific board of the Agency for Quality in Medicine, Berlin, Germany (2016–2024) and responsible coordinator of the German National Disease Management Guideline “Unipolar Depression”. He is also a member of the expert committee for the development of the Disease Management Program Depression at the Federal Joint Committee. He has been managing director of the German Network for Health Services Research (honorary) since 2022. He was the principal investigator of the COMET trial.

The remaining authors declare that there is no conflict of interest.

Manuscript received on 5 September 2024, revised version accepted on 16 January 2025

Corresponding author
Prof. Dr. med. Dr. phil. Martin Härter

References

1. Steel Z, Marnane C, Iranpour C, et al.: The global prevalence of common mental disorders: a systematic review and meta-analysis 1980–2013. Int J Epidemiol 2014; 43: 476–93

2. Jacobi F, Hofler M, Siegert J, et al.: Twelve-month prevalence, comorbidity and correlates of mental disorders in Germany: the mental health module of the German Health Interview and Examination Survey for Adults (DEGS1-MH). Int J Meth Psych Res 2014; 23: 304–19

3. Gustavsson A, Svensson M, Jacobi F, et al.: Cost of disorders of the brain in Europe 2010. Eur Neuropsychopharmacol 2011; 21: 718–79

4. Whiteford HA, Ferrari AJ, Degenhardt L, Feigin V, Vos T: The global burden of mental, neurological and substance use disorders: an analysis from the Global Burden of Disease Study 2010. PLoS One 2015; 10: e0116820

5. Roca M, Gili M, Garcia-Garcia M, et al.: Prevalence and comorbidity of common mental disorders in primary care. J Affect Disord 2009; 119: 52–8

6. Löwe B, Spitzer RL, Williams JB, Mussell M, Schellberg D, Kroenke K: Depression, anxiety and somatization in primary care: syndrome overlap and functional impairment. Gen Hosp Psychiat 2008; 30: 191–9

7. Jonker L, Thwaites R, Fisher SJ: Patient referral from primary care to psychological therapy services: a cohort study. Fam Pract 2019; 37: 395–400

8. Gaebel W, Kowitz S, Fritze J, Zielasek J: Use of health care services by people with mental illness: secondary data from three statutory health insurers and the German Statutory Pension Insurance Scheme. Dtsch Arztebl Int 2013; 110: 799–808

9. DGPPN, BÄK, KBV, et al.: S3-Leitlinie/Nationale VersorgungsLeitlinie Unipolare Depression—Langfassung. DGPPN, BÄK, KBV, AWMF. Berlin; 2015.

10. National Insitute for Health and Care Excellence (NICE): Common mental health problems: identification and pathways to care. Clinical Guideline 123; 2011.

11. Thota AB, Sipe TA, Byard GJ, et al.: Collaborative care to improve the management of depressive disorders: a community guide systematic review and meta-analysis. Am J Prev Med 2012; 42: 525–38

12. Archer J, Bower P, Gilbody S, et al.: Collaborative care for depression and anxiety problems. Cochrane Database Syst Rev 2012; 10: CD006525

13. Rivero-Santana A, Perestelo-Perez L, Alvarez-Perez Y, et al.: Stepped care for the treatment of depression: a systematic review and meta-analysis. J Affect Disord 2021; 294: 391–409

14. van Straten A, Hill J, Richards DA, Cuijpers P: Stepped care treatment delivery for depression: a systematic review and meta-analysis. Psychol Med 2015; 45: 231–46

15. van der Feltz-Cornelis CM, van Oppen P, Ader HJ, van Dyck R: Randomised controlled trial of a collaborative care model with psychiatric consultation for persistent medically unexplained symptoms in general practice. Psychother Psychosom 2006; 75: 282–9

16. Löwe B, Piontek K, Daubmann A, et al.: Effectiveness of a stepped, collaborative, and coordinated health care network for somatoform disorders (Sofu-Net): a controlled cluster cohort study. Psychosom Med 2017; 79: 1016–24

17. Rombouts SA, Conigrave JH, Saitz R, Louie E, Haber P, Morley KC: Evidence based models of care for the treatment of alcohol use disorder in primary health care settings: a systematic review. BMC Fam Pract 2020; 21: 1–17

18. Maehder K, Löwe B, Härter M, Heddaeus D, Scherer M, Weigel A: Management of comorbid mental and somatic disorders in stepped care approaches in primary care: a systematic review. Fam Pract 2019; 25; 36: 38–52

19. Oosterbaan DB, Verbraak MJPM, Terluin B, et al.: Collaborative stepped care v. care as usual for common mental disorders: 8-month, cluster randomised controlled trial. Br J Psychiatry 2013; 203: 132–9

20. Heddaeus D, Dirmaier J, Daubmann A, et al.: Study protocol for a cluster randomized, prospective, parallel-group, superiority trial to compare the effectiveness of a collaborative and stepped care model versus treatment as usual in patients with mental disorders in primary care: The COMET study. BMJ Open 2019; 9: e032408

21. DG-Sucht: S3-Leitlinie „Alkoholbezogene Störungen: Screening, Diagnose und Behandlung“: AWMF; 2015.

22. Bandelow B, Wiltink J, Alpers GW, et al.: Deutsche S3-Leitlinie Behandlung von Angststörungen. https://register.awmf.org/assets/guidelines/051-028l_S3_Angststörungen_2014-05_1.pdf (last accessed on 23 January 2025)

23. AWMF: S3 Leitlinie „Funktionelle Körperbeschwerden“ AWMF-Reg.-Nr. 051–001. https://register.awmf.org/de/leitlinien/detail/051-001 (last accessed on 23 January 2025).

24. NICE: Depression. The treatment and management of depression in adults (updated edition). Leicester: The British Psychological Society; 2010.

25. Bullinger M, Kirchberger I: SF-36. Fragebogen zum Gesundheitszustand. Göttingen: Hogrefe 1998.

26. Löwe B, Unützer J, Callahan CM, Perkins AJ, Kroenke K: Monitoring depression treatment outcomes with the Patient Health Questionnaire-9. Med Care 2004; 42: 1194–201

27. Spitzer R, Kroenke K, Williams J, Löwe B: A brief measure for assessing generalized anxiety disorder: the GAD-7. JAMA Intern Med 2006; 166: 1092–7

28. Löwe B, Spitzer RL, Zipfel S, Herzog W: Gesundheitsfragebogen für Patienten (PHQ-D): Manual und Testunterlagen. Karlsruhe: Pfizer 2002.

29. Toussaint A, Murray AM, Voigt K, et al.: Development and validation of the Somatic Symptom Disorder–b Criteria Scale (SSD-12). Psychosom Med 2016; 78: 5–12

30. Dybek I, Bischof G, Grothues J, et al.: The reliability and validity of the Alcohol Use Disorders Identification Test (AUDIT) in a German general practice population sample. J Stud Alcohol 2006; 67: 473–81

31. Scholl I, Hölzel L, Härter M, Dierks ML, Bitzer EM, Kriston L: Fragebogen zur Zufriedenheit in der ambulanten Versorgung—Schwerpunkt Patientenbeteiligung (ZAPA). Klinische Diagnostik und Evaluation 2011; 4: 50–62.

32. Roe D, Slade M, Jones N: The utility of patient-reported outcome measures in mental health. World Psychiatry 2022; 21: 56–7

33. Härter M, Watzke B, Daubmann A, et al.: Guideline-based stepped and collaborative care for patients with depression in a cluster-randomised trial. Sci Rep-UK 2018; 8: 9389

34. Muntingh A, Van Der Feltz-Cornelis C, Van Marwijk H, et al.: Effectiveness of collaborative stepped care for anxiety disorders in primary care: a pragmatic cluster randomised controlled trial. Psychother Psychosom 2013; 83: 37–44

35. Curth NK, Brinck-Claussen UO, Hjorthoj C, Davidsen AS, Mikkelsen JH, Lau ME, et al. Collaborative care for depression and anxiety disorders: results and lessons learned from the Danish cluster-randomized Collabri trials. BMC Fam Pract 2020; 21: 234

36. Uebelacker LA, Smith M, Lewis AW, Sasaki R, Miller IW: Treatment of depression in a low-income primary care setting with colocated mental health care. Fam Syst Health 2009; 27: 161–71

37. Maehder K, Werner S, Weigel A, et al.: How do care providers evaluate collaboration?—qualitative process evaluation of a cluster-randomized controlled trial of collaborative and stepped care for patients with mental disorders. BMC Psychiatry 2021; 21: 296

38. Watzke B, Heddaeus D, Steinmann M, Daubmann A, Wegscheider K, Härter M: Does symptom severity matter in stepped and collaborative care for depression? J Affect Disord 2020; 277: 287–95

39. Wiklund I: Assessment of patient-reported outcomes in clinical trials: the example of health-related quality of life. Fundam Clin Pharmaco 2004; 18: 351–63

40. Paterson C: Measuring outcomes in primary care: a patient generated measure, MYMOP, compared with the SF-36 health survey. BMJ 1996; 312: 1016–20

e1. Campbell MK, Piaggio G, Elbourne DR, Altman DG, Group C: Consort 2010 statement: extension to cluster randomised trials. BMJ 2012; 345: e5661

e2. Cohen J: Statistical power analysis for the behavioral sciences. Hillsdale, NJ, England: Lawrence Erlbaum Accociates 1988.

e3. Borm GF, Fransen J, Lemmens WAJG: A simple sample size formula for analysis of covariance in randomized clinical trials. J Clin Epidemiol 2007; 60: 1234–8

e4. Donner A: Some aspects of the design and analysis of cluster randomization trials. J Roy Stat Soc C-App 1998; 47: 95–113