Collaborative chronic care models for mental health conditions: cumulative meta-analysis and metaregression to guide...

Prior meta-analysis indicates that collaborative chronic care models (CCMs) improve mental and physical health outcomes for individuals with mental disorders. This study aimed to investigate the stability of evidence over time and identify patient and intervention factors associated with CCM effects to facilitate implementation and sustainability of CCMs in clinical practice.

We reviewed 53 CCM trials that analyzed depression, mental quality of life (QOL), or physical QOL outcomes. Cumulative meta-analysis and metaregression were supplemented by descriptive investigations across and within trials.

Most trials targeted depression in the primary care setting, and cumulative meta-analysis indicated that effect sizes favoring CCM quickly achieved significance for depression outcomes, and more recently achieved significance for mental and physical QOL. Four of 6 CCM elements (patient self-management support, clinical information systems, system redesign, and provider decision support) were common among reviewed trials, whereas 2 elements (health care organization support and linkages to community resources) were rare. No single CCM element was statistically associated with the success of the model. Similarly, metaregression did not identify specific factors associated with CCM effectiveness. Nonetheless, results within individual trials suggest that increased illness severity predicts CCM outcomes.

Significant CCM trials have been derived primarily from 4 original CCM elements. Nonetheless, implementing and sustaining this established model will require health care organization support. Although CCMs have typically been tested as population-based interventions, evidence supports stepped care application to more severely ill individuals. Future priorities include developing implementation strategies to support adoption and sustainability of the model in clinical settings while maximizing fit of this multicomponent framework to local contextual factors.