Patterns and predictors of 12-month treatment of common anxiety, mood, and substance use disorders in the World Mental Health (WMH) surveys: treatment in the context of perceived need
Bibliographic record
Abstract
BACKGROUND: Data from the World Mental Health (WMH) surveys on the coverage cascade has underscored the importance of perceived need for seeking treatment of mental disorders. However, little research has focused on treatment contact after adjusting for perceived need. We do so here in analysis of WMH data. METHODS: The WMH data considered here come from 25 community surveys implemented between 2001 and 2019 across 21 countries. n = 12,508 of the n = 117,739 respondents in these surveys aged 18 and older met criteria for one or more 12-month DSM-IV anxiety, mood, or substance use disorders assessed across all these surveys. Information was obtained about 12-month treatment of each disorder. The predictors considered were disorder type, socio-demographics, and history of prior treatment. RESULTS: Twelve-month treatment was obtained for 17.7% of the n = 18,702 12-month person-disorders in the sample, including 34.1% for the 46.5% with perceived need and 3.5% for the 54.5% without perceived need. After adjusting for perceived need, receiving treatment was most strongly associated with disorder characteristics (severity, and highest for major depressive, panic/agoraphobia, and generalized anxiety disorders; lowest for substance use disorders), health insurance, employment status (highest for students, the retired, and the unemployed/disabled), and several aspects of prior treatment. These associations were generally similar in cases with and without perceived need for treatment. 12-month treatment among cases who without perceived need and without history of prior treatment was rare (1.1%). CONCLUSIONS: Findings highlight the critical importance of perceived need for obtaining 12-month treatment in the context of other significant predictors involving complexity and severity of disorders and socio-demographic factors. The importance of prior treatment history was quite striking, as was the finding that absence of both perceived need and prior treatment history were associated with a nearly complete absence of treatment. Policy recommendations emerging from these results include the need to increase health literacy, reduce the stigmatization of mental disorder, enhance access through health insurance, and improve the quality of care given the clear evidence that prior experiences with treatment play an important role in determining the likelihood of again seeking treatment for current problems.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".