Service coverage for major depressive disorder: estimated rates of minimally adequate treatment for 204 countries and territories in 2021
Bibliographic record
Abstract
BACKGROUND: Access to effective treatment for major depressive disorder remains limited and difficult to track across place and time. We analysed the available data on minimally adequate treatment (MAT) for major depressive disorder globally with the aim of providing a useful metric against which to monitor national responses to the growing public health burden imposed by major depressive disorder. METHODS: MAT was defined as pharmacotherapy (1 month of medication, plus four visits to a medical doctor) or psychotherapy (eight visits with any professional). From existing reviews, we identified mental health surveys that assessed major depressive disorder within the general population as well as health service uptake by individuals with major depressive disorder. Data by ethnicity were not available. Estimates of MAT, antidepressant use, or use of any mental health service were extracted. The latter two estimates were adjusted to reflect likely MAT rates via a network meta-analysis. Adjusted MAT estimates were analysed via a Bayesian meta-regression using the Disease Modelling Meta-Regression (DisMod-MR 2.1) tool. This analysis estimated MAT coverage among people with major depressive disorder by age, sex, location, and year. Final MAT estimates were standardised by age and sex against the existing age and sex distribution of people with major depressive disorder globally. People with lived experience were involved in the design, preparation, interpretation, and writing of this manuscript. FINDINGS: The analysed dataset included 145 estimates from 32 studies, covering 31 countries, 14 regions, and six super-regions. The proportion of people with major depressive disorder receiving MAT globally in 2021 was 9·1% (95% uncertainty interval 7·2-11·6), with 10·2% (8·2-13·1) of females and 7·2% (5·7-9·3) of males with major depressive disorder receiving MAT. MAT coverage was highest in high-income locations (27·0% [21·7-34·4]), with Australasia having the highest rate (29·2% [21·4-40·8]). MAT coverage was lowest in sub-Saharan Africa (2·0% [1·5-2·6]), within which western sub-Saharan Africa (1·8% [1·4-2·5]) had the lowest coverage. Seven countries (Australia, Belgium, Canada, Germany, the Netherlands, South Korea, and Sweden) were estimated to have MAT coverage exceeding 30%, while 90 countries were estimated to have coverage lower than 5%. INTERPRETATION: Despite many gaps in the available data, estimates show that, globally, most individuals with major depressive disorder do not receive MAT. Services must improve to reach a global coverage that better meets the mental health needs of those with major depressive disorder. Urgent attention should be given to the scale-up of effective intervention strategies, especially in low-income and middle-income countries, as well as further research into better quality treatment options for major depressive disorder. We present a means by which the MAT gap for major depressive disorder can be quantified, to monitor and inform action by governments and international partners. FUNDING: Queensland Health and the Bill & Melinda Gates Foundation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".