AbstractSeveral care models have been developed to improve treatment for depression, all of which provide “enhanced” evidence-based care (EEC). The essential component of these approaches is Measurement-Based Care (MBC). Specifically, Collaborative Care (CC), and Algorithm-guided Treatment (AGT), and Integrated Care (IC) all use varying forms of rigorous MBC assessment, care management, and/or treatment algorithms as key instruments to optimize treatment delivery and outcomes for depression. This meta-analysis systematically examined the effectiveness of EEC versus usual care for depressive disorders based on cluster-randomized studies or randomized controlled trials (RCTs). PubMed, the Cochrane Library, and PsycInfo, EMBASE, up to January 6th, 2020 were searched for this meta-analysis. The electronic search was supplemented by a manual search. Standardized mean difference (SMD), risk ratio (RR), and their 95% confidence intervals (CIs) were calculated and analyzed. A total of 29 studies with 15,255 participants were analyzed. EEC showed better effectiveness with the pooled RR for response of 1.30 (95%CI: 1.13–1.50, I2 = 81.9%, P < 0.001, 18 studies), remission of 1.35 (95%CI: 1.11–1.64, I2 = 85.5%, P < 0.001, 18 studies) and symptom reduction with a pooled SMD of −0.42 (95%CI: −0.61–(−0.23), I2 = 94.3%, P < 0.001, 19 studies). All-cause discontinuations were similar between EEC and usual care with the pooled RR of 1.08 (95%CI: 0.94–1.23, I2 = 68.0%, P = 0.303, 27 studies). This meta-analysis supported EEC as an evidence-based framework to improve the treatment outcome of depressive disorders.Review registration: PROSPERO: CRD42020163668
An ontology of randomized controlled trials for evidence-based practice: content specification and evaluation using the competency decomposition method
