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Global Burden Disease Estimates for Major Depressive Disorders (MDD): A review of diagnostic instruments used in studies of prevalence

Lookup NU author(s): Dr Rosanna Lyus, Professor Allyson PollockORCiD

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Abstract

© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. Global Burden of Disease (GBD) estimates have significant policy implications nationally and internationally. Disease burden metrics, particularly for depression, have played a critical role in raising governmental awareness of mental health and in calculating the economic cost of depression. Recently, the World Health Organization ranked depression as the single largest contributor to global disability. The main aim of this paper was to assess the basis upon which GBD prevalence estimates for major depressive disorder (MDD) were made. We identify the instruments used in the 2019 GBD estimates and provide a descriptive assessment of the five most frequently used instruments. The majority of country studies, 356/566 (62.9%), used general mental health screeners or structured/semi-structured interview guides, 98/566 (17.3%) of the studies used dedicated depression screeners, and 112 (19.8%) used other tools for assessing depression. Thus, most of the studies used instruments that were not designed to make a diagnosis of depression or assess depression severity. Our results are congruent with and extend previous research that has identified critical flaws in the data underpinning the GBD estimates for MDD. Despite the widespread promotion of these prevalence estimates, caution is needed before using them to inform public policy and mental health interventions. This is particularly important in lower-income countries where resources are scarce.

Publication metadata

Author(s): Cosgrove L, Brhlikova P, Lyus R, Herrawi F, D'Ambrozio G, Abi-Jaoude E, Pollock AM

Publication type: Article

Publication status: Published

Journal: Community Mental Health Journal

Year: 2024

Pages: epub ahead of print

Online publication date: 01/07/2024

Acceptance date: 21/05/2024

ISSN (print): 0010-3853

ISSN (electronic): 1573-2789

Publisher: Springer Nature

URL: https://doi.org/10.1007/s10597-024-01302-6

DOI: 10.1007/s10597-024-01302-6

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