Effective Treatment for Mental and Substance Use Disorders in 21 Countries
Bibliographic record
Abstract
Importance: Accurate baseline information about the proportion of people with mental disorders who receive effective treatment is required to assess the success of treatment quality improvement initiatives. Objective: To examine the proportion of mental and substance use disorders receiving guideline-consistent treatment in multiple countries. Design, Setting, and Participants: In this cross-sectional study, World Mental Health (WMH) surveys were administered to representative adult (aged 18 years and older) household samples in 21 countries. Data were collected between 2001 and 2019 and analyzed between February and July 2024. Twelve-month prevalence and treatment of 9 DSM-IV anxiety, mood, and substance use disorders were assessed with the Composite International Diagnostic Interview. Effective treatment and its components were estimated with cross-tabulations. Multilevel regression models were used to examine predictors. Main Outcomes and Measures: The main outcome was proportion of effective treatment received, defined at the disorder level using information about disorder severity and published treatment guidelines regarding adequate medication type, control, and adherence and adequate psychotherapy frequency. Intermediate outcomes included perceived need for treatment, treatment contact separately in the presence and absence of perceived need, and minimally adequate treatment given contact. Individual-level predictors (multivariable disorder profile, sex, age, education, family income, marital status, employment status, and health insurance) and country-level predictors (treatment resources, health care spending, human development indicators, stigma, and discrimination) were traced through intervening outcomes. Results: Among the 56 927 respondents (69.3% weighted average response rate), 32 829 (57.7%) were female; the median (IQR) age was 43 (31-57) years. The proportion of 12-month person-disorders receiving effective treatment was 6.9% (SE, 0.3). Low perceived need (46.5%; SE, 0.6), low treatment contact given perceived need (34.1%; SE, 1.0), and low effective treatment given minimally adequate treatment (47.0%; SE, 1.7) were the major barriers, but with substantial variation across disorders. Country-level general medical treatment resources were more important than mental health treatment resources. Other than for the multivariable disorder profile, which was associated with all intermediate outcomes, significant predictors were largely mediated by treatment contact. Conclusions and Relevance: In addition to the gaps in treatment quality, these results highlight the importance of increasing perceived need, the largest barrier to effective treatment; the importance of training primary care treatment clinicians in recognition and treatment of mental disorders; the need to improve the continuum of care, especially from minimally adequate to effective treatment; and the importance of bridging the effective treatment gap for men and people with lower education.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".