Successful ingredients of effective Collaborative Care programs in low- and middle-income countries: A rapid review.

LMIC collaborative care depression health care delivery implementation integrated care mental health disorders mental health integration mhGAP outpatient mental health care substance use disorders

Journal

Global mental health (Cambridge, England)
ISSN: 2054-4251
Titre abrégé: Glob Ment Health (Camb)
Pays: England
ID NLM: 101659641

Informations de publication

Date de publication:
2023
Historique:
received: 11 08 2022
revised: 01 11 2022
accepted: 22 11 2022
medline: 19 10 2023
pubmed: 19 10 2023
entrez: 19 10 2023
Statut: epublish

Résumé

Integrating mental health care in primary healthcare settings is a compelling strategy to address the mental health treatment gap in low- and middle-income countries (LMICs). Collaborative Care is the integrated care model with the most evidence supporting its effectiveness, but most research has been conducted in high-income countries. Efforts to implement this complex multi-component model at scale in LMICs will be enhanced by understanding the model components that have been effective in LMIC settings. Following Cochrane Rapid Reviews Methods Group recommendations, we conducted a rapid review to identify studies of the effectiveness of Collaborative Care for priority adult mental disorders of mhGAP (mood and anxiety disorders, psychosis, substance use disorders and epilepsy) in outpatient medical settings in LMICs. Article screening and data extraction were performed using Covidence software. Data extraction by two authors utilized a checklist of key components of effective interventions. Information was aggregated to examine how frequently the components were applied. Our search yielded 25 articles describing 20 Collaborative Care models that treated depression, anxiety, schizophrenia, alcohol use disorder or epilepsy in nine different LMICs. Fourteen of these models demonstrated statistically significantly improved clinical outcomes compared to comparison groups. Successful models shared key structural and process-of-care elements: a multi-disciplinary care team with structured communication; standardized protocols for evidence-based treatments; systematic identification of mental disorders, and a stepped-care approach to treatment intensification. There was substantial heterogeneity across studies with respect to the specifics of model components, and clear evidence of the importance of tailoring the model to the local context. This review provides evidence that Collaborative Care is effective across a range of mental disorders in LMICs. More work is needed to demonstrate population-level and longer-term outcomes, and to identify strategies that will support successful and sustained implementation in routine clinical settings.

Identifiants

pubmed: 37854388
doi: 10.1017/gmh.2022.60
pii: S2054425122000607
pmc: PMC10579696
doi:

Types de publication

Journal Article Review

Langues

eng

Pagination

e11

Informations de copyright

© The Author(s) 2023.

Déclaration de conflit d'intérêts

The authors declare none.

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Auteurs

Jessica Whitfield (J)

Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, Seattle, WA, USA.
Advancing Integrated Mental Health Solutions (AIMS) Center, University of Washington, Seattle, WA, USA.

Shanise Owens (S)

Department of Health Systems and Population Health, University of Washington School of Public Health, Seattle, WA, USA.

Amritha Bhat (A)

Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, Seattle, WA, USA.

Bradford Felker (B)

Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, Seattle, WA, USA.

Teresa Jewell (T)

University of Washington Health Sciences Library, University of Washington, Seattle, WA, USA.

Lydia Chwastiak (L)

Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, Seattle, WA, USA.
Advancing Integrated Mental Health Solutions (AIMS) Center, University of Washington, Seattle, WA, USA.
Department of Global Health, University of Washington School of Public Health, Seattle, WA, USA.

Classifications MeSH