March 2009

Evidence-based Synthesis Program Department of Veterans Affairs

Health Services Research & Development Service

Prepared for: 

Department of Veterans Affairs

Veterans Health Administration

Health Services Research 

& Development Service

Washington, DC 20420 

Prepared by:

Greater Los Angeles Veterans Affairs Healthcare 

System/Southern California/RAND 

Evidence-based Practice Center 

Los Angeles, CA

Determining Key Features of 

Effective Depression Interventions

Investigators

Lisa V. Rubenstein, MD, MSPH

Professor of Medicine, VA Greater Los Angeles and UCLA

PI, HSR&D Center of Excellence for the Study of Healthcare Provider 

Senior Scientist, RAND Corporation

Los Angeles and Santa Monica, CA

John W. Williams Jr., MD, MHS

Professor of Medicine and Psychiatry, Durham VA Medical Center and 

Duke University

Director, Duke Evidence-Based Practice Center 

Durham, NC

Marjorie Danz, MD

Associate Scientist

RAND Corporation and VA Greater Los Angeles

HSR&D Center of Excellence for the Study of Healthcare Provider 

Behavior

Paul Shekelle, MD, PhD

Professor of Medicine, VA Greater Los Angeles and UCLA

Director, Evidence-Based Practice Center RAND Corporation

Los Angeles and Santa Monica, CA

Statistician

Marika Suttorp, MS

RAND Corporation, Santa Monica, CA

Research Assistant

Breanne Johnsen, BA

RAND Corporation, Santa Monica, CA
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This report is based on research conducted by the Greater Los Angeles Veterans Affairs 

Healthcare System and Southern California Evidence-based Practice Center (EPC) under 

contract to the Department of Veterans Affairs. The findings and conclusions in this 

document are those of the author(s) who are responsible for its contents; the findings and 

conclusions do not necessarily represent the views of the Department of Veterans Affairs. 

Therefore, no statement in this article should be construed as an official position of the 

Department of Veterans Affairs.

This report is intended as a reference and not as a substitute for clinical judgment. 

This report may be used, in whole or in part, as the basis for development of clinical 

practice guidelines and other quality enhancement tools, or as a basis for reimbursement 

and coverage policies. The Department of Veterans Affairs endorsement of such 

derivative products may not be stated or implied.
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Evidence Synthesis For Determining Key Features 

of Effective Depression Interventions

Executive Summary

Background

Current clinical guidelines for depression address depression treatment for patients detected 

in primary care (Agency for Health Care Policy and Research 1993; Schulberg, Katon et al. 

1998; Agency for Healthcare Policy and Research Depression Guideline Panel 2000); VA/DOD 

depression guidelines (http://vaww.oqp.med.va.gov); and NICE guidelines (NICE guidelines 

http://www.nice.org.uk/Guidance/CG23) Research to date indicates that, under usual care 

conditions, less than half of primary care patients found to have major depression complete 

minimally adequate medications or psychotherapy (Wells, 2000; Charbonneau, 2003). A variety 

of organizational changes aimed at improving care for depression in primary care have been 

tested. Yet evidence-based guidance for healthcare organizations and their primary care practices 

about which organizational changes are necessary for achieving improved depression outcomes 

is lacking. The purpose of this review is to establish a basis for organizational guidelines or best 

practices for achieving improved depression care.

The collaborative care model for depression has been extensively studied, and found to be both 

effective and cost-effective in prior meta-analysis (Gilbody, Bower et al. 2006; Gilbody, Bower 

et al. 2006). Collaborative care models are organizational interventions designed to remedy 

known deficits in current depression care (Hepner, Rowe et al. 2007). These multifaceted models 

are loosely defined as involving collaboration between providers from different specialties to 

provide appropriate, timely depression care (Craven, 2002) or as involving two of three types of 

professionals (a case manager, a primary care clinician, and a mental health specialist) (Gilbody, 

2006) working collaboratively within primary care. Thus, while all applications of this model are 

similar in focusing on supporting effective management of primary care patients detected outside 

of a mental health specialty setting, the specific features of the model vary from study to study. 

These variations make it difficult for care settings to know what features of the models tested 

and found to be effective in randomized trials of collaborative care are essential for achieving the 

expected effects. 

Collaborative care definitions like these have been directed primarily at staffing (e.g., the 

presence of case manager or mental health specialist). Current theories of chronic illness care, 

however, postulate that key additional organizational changes are required to achieve consistent, 

sustainable improvement (Institute of Medicine 2001) (Bodenheimer, 2002). When the multiple 

facets of collaborative care models are considered, most can be considered specific applications 

of the general, across-disease chronic illness care model (Wagner, Glasgow et al. 2001). This 

review focuses on high quality depression care randomized trials that involved at least one 

change in the organization of care as described in the chronic illness care model (Williams, 

Gerrity et al. 2007). 
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Our main research question was whether there are specific design features of collaborative 

care interventions that are consistently associated with greater impact on depression symptoms 

compared to a usual care control group. We also aimed to explore additional outcomes 

including patient satisfaction and functioning. In addition, we asked whether there were specific 

design features of randomized trial evaluations of collaborative care that were associated with 

consistently greater effects. Secondarily, we aimed to assess whether any patient characteristics, 

such as comorbidities, were associated with differential collaborative care effects, and the degree 

to which model effects persisted over time. We investigated these goals based on the following 

research questions.

.

1) Primary Research Question: What is the core set of intervention features that 

characterize collaborative care interventions, and which additional features are most 

linked to enhanced outcome effects? 

2) Secondary Research Question: Are there specific evaluation features among randomized 

trials of collaborative care that are associated with effect size differences, independently of 

intervention features? 

3) Secondary Research Question: To what extent is collaborative care more effective than 

usual care for decreasing depressive symptoms among patients with comorbid mental health 

conditions (PTSD, dementia, anxiety, dysthymia, substance abuse) or medical conditions? 

Methods

We used a set of articles identified and preliminarily reviewed as part of an earlier, nonquantitative literature review on depression care models (Williams, Gerrity et al. 2007) to carry 

out quantitative meta-regression analysis of collaborative care features. Studies were high 

quality randomized trials of depression collaborative care interventions compared to usual care 

that incorporated at least two features of the chronic illness care model. At least one of these 

features had to directly support patients in completing depression treatment. We did not review 

studies that only sought to change primary care clinician behavior (e.g., using reminders), 

without an additional patient-directed component, such as care management. We contacted 

authors extensively to identify, clarify, or verify study variables such as chronic illness care 

features or patient population characteristics.

We began our analyses by assessing correlations between features. For study outcomes, we 

evaluated the effect size across studies for changes in depression symptoms, and relative risk 

across studies for changes in rates of resolution of depression. For these analyses we used study 

effect sizes comparing intervention to usual care arms as the unit of analysis. The effect size 

analyses treated short (six weeks to four months), medium (five to eight months), and long (nine 

to twelve months) outcomes separately. We also measured intervention impact (high, medium, 

low and little or none) for each study based on reviewer ratings of de-identified sets of study 

outcomes, including adherence, patient satisfaction, and functioning. We eliminated variables 

with inadequate distributions for meaningful quantitative analysis, using a rule of thumb of at 

least three studies per variable category. We carried out univariate and multivariate regression to 
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determine relationships between intervention and evaluation features and effectiveness. .

Finally, we conducted cross-case qualitative analysis (Miles and Huberman 1994) of intervention 

and evaluation features, including comorbidities, against intervention impact. 

Results

Of 1464 articles identified, reviewers deemed 138 as potentially relevant. From this group, we 

identified 28 high quality randomized controlled trials that met inclusion criteria for collaborative 

depression care based on the chronic illness care model.

Overall Impact Effectiveness and Impact

Overall, the experimental groups in selected studies showed improvement compared to usual 

care. Twenty of 28 interventions improved depression outcomes over 3–12 months (an 18.4% 

median absolute increase in patients with 50% improvement in symptoms; range, 8.3–46%). 

Because of heterogeneity in outcome time frames and measures across studies, we could not 

analyze effect sizes for all 28 studies together. Our intervention impact measure enabled us 

to cross-check our results for all 28 studies as a group. The impact measure was significantly 

associated with both depression symptom and resolution results as measured using effect size 

and relative risk. 

Regression Results 

We found that not all studies could provide suitable information for each effectiveness analysis. 

21 of 28 studies were suitable for assessing short-term; 18 for assessing intermediate-term; 

and 10 for assessing long-term effects on depression symptoms. 18 were suitable for assessing 

relative risk for depression resolution. We found that studies were too heterogeneous in 

their assessments of psychiatric comorbidities and demographics to evaluate these variables 

quantitatively. Too few studies reported on medical comorbidities to evaluate these either 

qualitatively or quantitatively. We identified some intervention features as too correlated for 

regression analysis. Initial univariate regression results evaluating the relationship between 

intervention features and effects on depression symptoms and resolution showed active patient 

self-management support as the single statistically significant intervention characteristic 

associated with improved depression symptoms and depression resolution. Other individual 

collaborative care intervention features among the five we evaluated were not significantly 

related to outcomes. We found no individual intervention feature associated with longer term 

effects on depression symptoms. Among evaluation features, enrolling patients for the evaluation 

through screening was significantly associated with more positive effects on outcomes.

Cross-Case Analysis Results

With between 10 and 21 studies in each regression analysis against study effect size, we did 

not have the sample size to fully explore potential interactions and associations between 

intervention features quantitatively. In addition, some variables characterized too large or too 

small a proportion of the studies to provide adequate variation for valid regression analysis. To 

understand our initial regression results, we therefore carried out extensive cross-case analyses 

on our full sample of 28 studies looking at combinations of characteristics versus our impact 
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measure. 

Our cross-case analyses enabled us to evaluate features that occurred together in large 

proportions of higher impact studies (the 20 studies with high, medium or low impact versus the 

8 with little or no impact). We considered features present in 80% or more of the higher impact 

studies to be core features of effective collaborative care models. Our analyses identified six 

collaborative care model intervention core features. These were: 

○ Primary care clinicians actively involved in patient management

○ Mental health specialists actively involved in patient management

○ Care managers assessed patient symptoms at baseline with a standardized scale

○ Care managers assessed patient symptoms at follow-up with a standardized scale

○ Care managers assessed treatment adherence at follow-up 

○ Collaborative care intervention included at least 16 weeks of active patient follow-up

We also confirmed our quantitative results regarding patient self-management support. Higher 

impact studies tended to include active patient self-management support by a care manager 

or mental health professional, versus passive self-management support such as providing 

educational brochures.

We found no evaluation design features other than those related to psychiatric comorbidities 

(excluding bipolar disorder and psychosis) that characterized more than 80% of high impact 

studies. 77% of high impact studies excluded patients based on substance abuse. Nearly 80% of 

all 28 studies excluded psychosis and bipolar disorder and nearly all included anxiety. 20% of 

studies did not mention PTSD; only three of the remaining studies excluded PTSD patients.

Conclusions

Guidelines for sites intending to implement collaborative care for depression should identify 

primary care and mental health specialty clinician involvement; care manager assessment of 

symptoms at baseline and follow-up using a structured instrument; care manager follow-up 

assessment of treatment adherence; and active follow-up for at least 16 weeks as core features 

of current evidence-based models. Guidelines should further recommend inclusion of active 

self-management support, such as elements of patient activation, cognitive behavioral or 

problem-solving therapy, or motivational techniques, for additional improvement in outcomes. 

No evidence is available to support using collaborative care for depression with comorbid 

psychosis or bipolar disorder, and few studies addressed substance abuse. Future research 

testing collaborative care models for depression should assess effects of medical and psychiatric 

comorbidities, especially substance abuse.