Logo image
Patterns and predictors of the transition between minimally adequate treatment and effective treatment coverage for mental disorders: results from the World Mental Health Survey
Journal article   Open access   Peer reviewed

Patterns and predictors of the transition between minimally adequate treatment and effective treatment coverage for mental disorders: results from the World Mental Health Survey

Alan E Kazdin, Julia R Pozuelo, Meredith G Harris, Dan J Stein, Maria Carmen Viana, Irving Hwang, Timothy L Kessler, Sophie M Manoukian, Nancy A Sampson, Sergio Aguilar-Gaxiola, …
International journal of mental health systems, Vol.20, 7
24/02/2026
Handle:
https://hdl.handle.net/10523/49906

Abstract

Adequacy of treatment World Mental Health Survey Consortium Mental disorders treatment Cross-national Effective treatment coverage
Background: The quality of mental disorder treatment varies widely, with many patients not receiving treatments based on evidence-based guidelines. We examine data from the World Mental Health (WMH) surveys to investigate prevalence and correlates of receiving effective treatment coverage (ETC) among patients receiving minimally adequate treatment (MAT) in the 12 months before interview. Methods: Data come from 25 WMH surveys carried out in 21 countries that included n = 1,119 participants who met the criteria for at least one of nine 12-month disorders considered here who received MAT for n = 2,313 disorders. MAT was defined as either (i) medication with 4 + healthcare visits or (ii) 8 + counseling sessions. ETC was defined as a subset of MAT that additionally required (i) medication appropriate for the disorder (e.g., mood stabilizers, anticonvulsant, or antipsychotic for bipolar disorder) taken with adequate control and adherence; and/or (ii) 8 + counseling sessions with a mental healthcare provider. Multivariable regression analysis with person-disorder treated as the case was used to examine associations of socio-demographic, disorder-related, and treatment-related factors with receiving ETC given MAT. Results: Fewer than half (47.1%) the cases with MAT received treatment qualifying as ETC. The strongest predictors of ETC given MAT were high patient education, mild/moderate disorder severity, treatment by a mental health specialist rather than primary care provider, and receipt of combined treatment with both medication and counseling rather than only one of these types of treatment. Importantly, combined treatment was associated with a significantly higher relative-risk of ETC if it was provided by a psychiatrist rather than a combination of a general medical provider and a non-psychiatrist mental health provider. Conclusions: Noteworthy limitations include the data being cross-sectional, the predictor set being restricted, and the outcome being defined in terms of structural characteristics rather than fidelity of implementation. Within the context of these limitations, results suggest that fewer than half of cases with minimally adequate treatment receive treatment meeting published guidelines for effective treatment coverage. This finding underscores the importance of improving treatment. Future research should focus on targets to improve each stage of the help-seeking process beginning with entry into treatment through receipt of ETC.
pdf
s13033-026-00698-w1.68 MBDownloadView
Published (Version of record) Open Access CC BY-NC-ND V4.0  — You are free to Share - copy and redistribute the material in any medium or format. Under the following terms: Attribution - You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. NonCommercial - You may not use the material for commercial purposes. NoDerivatives - If you remix, transform, or build upon the material, you may not distribute the modified material.
url
https://doi.org/10.1186/s13033-026-00698-wView
Published (Version of record) Open CC BY-NC-ND V4.0  — You are free to Share - copy and redistribute the material in any medium or format. Under the following terms: Attribution - You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. NonCommercial - You may not use the material for commercial purposes. NoDerivatives - If you remix, transform, or build upon the material, you may not distribute the modified material.

Metrics

1 Record Views

Details

Logo image